Bacterial Pneumonia Score (BPS) Guided Antibiotic Use in Children With Pneumonia and Pneumococcal Vaccine: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Bacterial Pneumonia Score (BPS) Guided Antibiotic Use in Children With Pneumonia and Pneumococcal Vaccine (NCT01875731), a randomized na study. The primary outcome is Use of Antibiotics in Each Group.

2. Clinical Question

The posted primary results show 9 of 33 participants in the Bacterial Pneumonia Score group and 21 of 32 in the guideline group with antibiotic use. The recalculated, unadjusted risk difference for guideline compared with Bacterial Pneumonia Score was 0.384, with a 95% confidence interval from 0.160 to 0.608. This describes a higher observed proportion with antibiotic use in the guideline group in this trial. It does not establish why the groups differed or whether the finding would be the same in other settings.

3. Trial Design

The registry describes a randomized, single-masked, parallel-group study. Participants were allocated to one of two strategies: Bacterial Pneumonia Score-guided antibiotic use or guideline-guided antibiotic use. The registry lists enrollment as 66. The primary outcome results use denominators of 33 in the Bacterial Pneumonia Score group and 32 in the guideline group. The reason the primary-outcome denominators total less than the registered enrollment is not reported in the registry record.

The study began in 2013-07, had a primary completion date of 2014-05, and is marked completed. Its phase is listed as NA. The sponsor was Hospital General de Niños Pedro de Elizalde. The registry describes the intervention types as behavioral and the model as parallel. Although masking is listed as single, which people were masked and how masking was implemented are not reported in the registry record.

The primary outcome was antibiotic use at day 7 from baseline. A secondary outcome measured treatment failure at 1, 2, 5, 7 and 10 days from baseline. The registry lists one analysis for each outcome and reports chi-squared analyses using odds ratios. It does not report details needed to fully reconstruct the trial conduct, such as the randomization procedure, allocation concealment, protocol deviations, or analysis population. Those features should not be inferred from the design labels alone.

The central design contrast is between two care strategies, not between a particular antibiotic and a placebo. The endpoint therefore evaluates how often antibiotics were used under the assigned strategies. Because this is a randomized comparison, its interpretation depends on how allocation, follow-up, outcome ascertainment, and analysis were carried out; those operational details are not reported in the registry record.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

Reasons for not completing this period:

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Use of Antibiotics in Each Group, measured over At day 7 from baseline.

The primary endpoint counted participants who had used any antibiotic at any time after diagnosis, with the outcome assessed at day 7 from baseline. It is a binary outcome at the participant level: each participant either met the definition of antibiotic use or did not. The reported arm results are counts, not antibiotic courses, doses, or treatment duration.

This endpoint directly reflects the study's stated objective of comparing antibiotic use under score-guided and guideline-guided strategies. Counting participants with any use gives a simple measure of how often antibiotic exposure occurred during the stated period. It does not capture how long treatment continued, whether treatment was changed, or the reason an antibiotic was started. Those details are not reported in the registry record.

The endpoint should also be kept distinct from clinical outcomes. Antibiotic use measures a care decision or exposure, while treatment failure was registered as a separate secondary outcome. A difference in antibiotic use alone does not establish a difference in recovery, complications, or safety. The registry lists the primary timeframe as at day 7 from baseline and describes the outcome as any antibiotic use at any time after diagnosis; the exact ascertainment procedures are not reported in the registry record.

7. Results Posted in the Registry

• Guideline: 65.6% (21/32) versus 27.3% (9/33) on control; risk difference 38.4 percentage points (95% CI 16.0 to 60.8), risk ratio 2.41 (1.31–4.43); unadjusted two-proportion z-test p = 0.002.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Treatment Failure in Each Group

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Guideline: 12.5% (4/32) versus 9.1% (3/33) on control; risk difference 3.4 percentage points (95% CI -11.7 to 18.5), risk ratio 1.38 (0.33–5.66); unadjusted two-proportion z-test p = 0.658.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Guideline and the control. The interval gives the range of effects that remain compatible with the data.

Treatment failure is the secondary outcome posted in the registry. Across the two groups, the event counts are close, and the recalculated comparison is uncertain; the registry's own analysis is also statistically inconclusive. Taken together, the secondary findings do not provide a precise estimate of a difference in treatment failure. They also do not establish equivalence or rule out differences that the study may not estimate precisely.

This pattern complements, but does not reverse or validate, the primary finding. Antibiotic use and treatment failure are distinct outcomes: one concerns exposure to antibiotics and the other concerns a clinical event. The primary comparison can describe a difference in antibiotic use without establishing that the groups had the same clinical course. Conversely, an uncertain secondary comparison does not demonstrate that reducing antibiotic use had no relation to clinical outcomes.

Secondary results generally carry less weight than a prespecified primary endpoint because they may be exploratory and because testing several outcomes or time points can increase the chance of finding an apparently notable result by chance. The registry lists one secondary outcome, measured at multiple stated times, but does not report whether the analysis plan specified a single summary comparison, how time points were handled, or whether multiplicity was considered. The posted analyses should therefore be interpreted as secondary evidence, not as independent confirmation of the primary result.

The trial's record does not provide further secondary clinical outcomes or detail on how treatment failure was defined and assessed. Without those details, it is not possible to judge whether event ascertainment was blinded, how repeated assessments were summarized, or how incomplete follow-up may have affected the comparison. The broad interpretation is consequently limited: the posted secondary results do not show a clear between-group difference, but their uncertainty and reporting limitations remain important.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Treatment Failure in Each Group

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is binary because each participant is counted as either having used any antibiotic or not. For such an outcome, the arm-specific proportion is the number with the event divided by the number in that arm. The registry gives the event counts and denominators, and the recalculated comparison expresses the difference in those proportions as a risk difference. A positive risk difference for guideline versus Bacterial Pneumonia Score means the observed event proportion was higher in the guideline group.

What does the risk difference describe?

The risk difference is an absolute comparison between event proportions. In this trial, its estimate is 0.384, with a 95% confidence interval from 0.160 to 0.608. It is expressed on the probability scale, so it describes the separation between the two observed group proportions rather than a relative change. Its confidence interval gives a range of values compatible with the data under the calculation's assumptions. The interval does not mean that the true value has a 95% probability of falling inside this particular calculated interval.

How does an odds ratio differ?

An odds ratio compares the odds of the event between groups. Odds are not the same as probabilities, so an odds ratio and a risk difference describe the contrast on different scales. The registry reports an odds ratio for each outcome using a chi-squared method. For the primary outcome, that reported estimate is 5.09, with a 95% confidence interval from 1.57 to 16.8. The unadjusted recalculation gives an odds ratio of 5.091, with a 95% confidence interval from 1.768 to 14.66. The values are close as point estimates, but the confidence limits differ. The registry does not report the details needed to account for that difference.

What does a p-value convey?

A p-value describes how unusual data at least as discrepant as those observed would be under a specified null model and its assumptions. It is not the probability that the null hypothesis is true, nor does it measure the size or importance of an effect. The registry reports p = 0.01 for the primary chi-squared analysis; the unadjusted recalculation gives p = 0.002. Both should be read alongside the effect estimate and confidence interval, not as standalone measures of evidence. The registry does not report whether the listed analyses were prespecified or whether any multiplicity adjustment was used.

Why can analysis methods yield different intervals?

Methods can differ in how they calculate uncertainty, especially with limited event counts or modest group sizes. The posted registry analysis and the unadjusted recalculation need not produce identical confidence intervals or p-values. The registry identifies a chi-squared method for the reported odds ratios but does not report all calculation details. The recalculated estimates are simple unadjusted comparisons from the posted arm counts. They are not a substitute for an analysis using participant-level data or undocumented design features.

What does over-dispersion mean in general?

Over-dispersion is a general concern for count outcomes when variability exceeds what a chosen probability model expects. It can make standard errors too small if a model assumes less variability than the data contain. It is not a central feature of this primary endpoint, which is reported as a binary participant count, and the registry does not report an over-dispersion assessment. More generally, the outcome type and analysis method should match the data structure; here, the endpoint is whether a participant used any antibiotic.

What do the secondary analyses add?

Treatment failure is another binary participant outcome, assessed at the registered time points. The registry reports a chi-squared odds-ratio analysis, while the unadjusted recalculation also gives a risk difference and confidence interval. These analyses address the secondary endpoint, not the primary antibiotic-use endpoint. Their uncertainty and their secondary status matter when weighing them against the primary analysis. The registry does not report multiplicity procedures, missing-data handling, or the analysis population, so those aspects cannot be reconstructed from the posted results.

11. Confidence Intervals and Precision

Precision describes how narrowly the data estimate a comparison. The primary risk-difference estimate is 0.384, and its 95% confidence interval runs from 0.160 to 0.608. This interval is compatible with a range of positive differences, rather than identifying one exact effect size. Its width matters: the estimate is not known with the same certainty as it would be if the interval were narrower. The interval also reflects the calculation and assumptions used for the unadjusted comparison.

The registry's primary odds-ratio interval is 1.57 to 16.8 around an estimate of 5.09. The recalculated odds-ratio interval is 1.768 to 14.66 around an estimate of 5.091. These intervals both indicate uncertainty in the magnitude of the odds contrast, and they are not identical. The registry does not report sufficient detail to identify why the interval limits differ. The recalculated risk ratio is 2.406, with a 95% confidence interval from 1.306 to 4.433; this is yet another scale for the same binary outcome and should not be confused with either the risk difference or odds ratio.

For treatment failure, the recalculated risk difference is 0.034, with a 95% confidence interval from -0.117 to 0.185. That interval includes no difference and also allows effects in either direction. The registry's odds-ratio interval, from 0.1 to 4.09, is likewise broad. These estimates do not precisely determine the size or direction of a treatment-failure contrast. A result that is not statistically clear does not prove equivalence or establish that the groups are the same.

Confidence intervals quantify sampling uncertainty under the specified method; they do not account for every possible source of error. Issues such as missing outcomes, departures from assigned strategies, or measurement limitations may affect interpretation, and their handling is not reported in the registry record. The sample sizes in the two outcome groups also limit how finely these data can estimate differences.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size calculations are planning illustrations based on the observed effect, not the trial's original design calculations. The listed assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is a stated fraction of the observed effect. The original trial's sample-size assumptions and justification are not reported in the registry record.

For these binary outcomes, the figures are participants per arm, not total events. The 80% power column describes the number of participants per arm that the planning calculation estimates would be needed to have an 80% chance of detecting the specified effect under its assumptions. The 90% power column gives the corresponding planning target for a 90% chance. These are design targets for hypothetical future studies, not descriptions of the completed trial's probability of finding a result.

The rows for 100%, 75% and 50% of the observed effect illustrate how the planned effect size changes the required sample size. Smaller effects require more participants to detect reliably, all else held to the listed assumptions. For the primary outcome, planning for 100% of the observed effect gives 26 participants per arm for 80% power and 34 participants per arm for 90% power; planning for 50% of the observed effect gives 99 participants per arm for 80% power and 131 participants per arm for 90% power. These calculations are not observed power. They depend on the observed effect, which can be an unstable basis for planning, and should be read as illustrations rather than as a retrospective verdict on the trial.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.273; treatment proportion p₁ = 0.656; average p̄ = 0.464.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.7053 = 1.3824.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6511 = 0.5480.
4. n = (1.3824 + 0.5480)² / (0.3835)² = 25.3, rounded up to 26 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.272727272727273                          # control proportion
p1 <- 0.65625                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(1875731)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 26 (80% power), 34 (90% power)
 75% of observed effect: n per arm = 45 (80% power), 60 (90% power)
 50% of observed effect: n per arm = 99 (80% power), 131 (90% power)
power.prop.test: 25.3 
Simulated power at n = 26 per arm: 0.799 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Analyses Reported by the Sponsor

The registry's own primary analysis reports a chi-squared odds ratio of 5.09, with a 95% confidence interval from 1.57 to 16.8 and p = 0.01. The unadjusted recalculation from the posted counts gives an odds ratio of 5.091, with a 95% confidence interval from 1.768 to 14.66 and p = 0.002. The recalculated risk-difference result is 0.384, with a 95% confidence interval from 0.160 to 0.608. These measures use different scales, while the sponsor's reported result and the recalculated odds ratio are both odds-based contrasts.

The point estimates for the odds ratio are very close, but the confidence limits and p-values differ. The registry does not report enough information about the sponsor's calculation to explain the difference, and no participant-level data are available in the record to reproduce that analysis. The recalculation uses only the posted summary counts and is unadjusted. It does not supersede the analysis reported by the registry, and neither result should be presented as though the calculation procedures were identical.

For treatment failure, the registry reports an odds ratio of 0.7, with a 95% confidence interval from 0.1 to 4.09 and p = 0.71. The unadjusted recalculation also produces an imprecise comparison. The record does not describe adjustments, model specifications, or analysis-population details for either outcome. Interpretation should therefore keep the sponsor-reported estimates distinct from the simple recalculated comparisons.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists a primary outcome and one secondary outcome. The secondary outcome is assessed at 1, 2, 5, 7 and 10 days from baseline, so repeated time points are part of the outcome record. The registry does not report whether one time point was designated as the main secondary comparison, whether repeated assessments were combined, or whether a multiplicity adjustment was planned. Without that information, the possibility of multiple statistical comparisons cannot be fully evaluated.

Multiplicity matters because conducting several tests can increase the chance that at least one result appears unusual under a null model. This is a general statistical concern, not evidence that a particular reported result is invalid. The primary outcome is identified separately, but the registry does not report a full hierarchy of outcomes, prespecification details, or an analysis plan that would show how the primary and secondary tests were to be interpreted together.

Missing-data handling is not reported in the registry record. The registered enrollment is 66, while the primary outcome denominators are 33 and 32. The record does not explain the difference, identify whether all randomized participants were included in the analyses, or describe how any unavailable outcome data were handled. It would be inappropriate to infer the reason from the denominators alone.

The analysis population is also not reported. The record does not say whether analyses followed assigned groups regardless of treatment received, included only participants who followed the assigned strategy, or used another population definition. Such choices can affect interpretation in randomized trials. The registry identifies random allocation and reports chi-squared analyses, but does not report allocation concealment, protocol deviations, adjustment for baseline characteristics, or sensitivity analyses. These omissions limit assessment of how robust the posted comparisons are and whether all randomized participants contributed to each result.

16. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

The adverse-event record lists 0 serious adverse events among 33 participants at risk in the Bacterial Pneumonia Score group and 0 among 32 at risk in the guideline group. It also lists 0 other adverse events among 33 and 0 among 32, respectively. No serious adverse-event terms are listed. These are the recorded counts in the registry and should be understood in relation to the stated numbers at risk.

Zero recorded events in these groups does not prove that no adverse events occurred outside the reporting and follow-up captured by the registry, nor does it establish that the strategies have identical safety profiles. The record does not describe event definitions, ascertainment procedures, severity grading beyond the serious-event category, or the completeness and duration of safety follow-up. Death counts are not reported in the registry record. The safety figures therefore provide a limited account of recorded events rather than a comprehensive assessment of risk.

17. What the Result Does, and Does Not, Mean

The posted primary data show that antibiotic use was recorded for fewer participants in the Bacterial Pneumonia Score group than in the guideline group. The unadjusted risk-difference estimate and its confidence interval indicate a positive difference for guideline versus Bacterial Pneumonia Score in this trial. The registry's own odds-ratio analysis also reports a difference between groups. These are descriptions of the observed comparison, not proof that the same contrast will occur in other populations or settings.

The outcome measures antibiotic use, not clinical benefit. A lower recorded frequency of antibiotic use does not by itself show that participants recovered equally, had fewer complications, or experienced better overall outcomes. Treatment failure was reported separately, but that secondary comparison is imprecise and does not settle questions about clinical outcomes. The registry does not report enough detail to assess other possible benefits or harms.

The findings also do not identify which elements of care caused the difference. The strategies differed in how antibiotic use was guided, but the registry does not report adherence, clinician decision processes, or the specific guidance criteria. Nor does the posted result establish the effect of a particular antibiotic or a particular treatment duration.

Confidence intervals communicate the uncertainty around estimates, and the secondary results remain broad. The registered sample size and outcome denominators are modest in relation to the range of possible outcomes, while the exact missing-data and analysis-population procedures are not reported. The estimates should therefore be interpreted as evidence about the recorded groups in this completed study, with uncertainty and reporting limits, rather than as a universal treatment rule.

No medical decision for an individual child follows from these registry results alone. The record supports a statistical reading of the comparison made in this trial; broader clinical interpretation would require context and evidence beyond the posted results.

18. Why This Trial Matters Statistically

This trial is statistically informative because it makes the target outcome explicit: whether a participant used any antibiotic by day 7. That binary endpoint can be interpreted directly from the arm counts and compared on absolute and relative scales. Keeping the endpoint's meaning clear helps prevent a difference in treatment exposure from being mistaken for a difference in health outcome.

The record also illustrates why effect estimates, confidence intervals, and p-values should be considered together. The primary results indicate a group contrast, but the magnitude is not known exactly. The registry's odds-ratio analysis and the unadjusted recalculation have similar point estimates but different confidence limits and p-values, showing why analysis details matter when reproducing a result from summary data.

The secondary outcome provides an important reminder that a clear result on one endpoint does not answer every question. Treatment failure is clinically distinct from antibiotic use, and its estimate is imprecise. The two outcomes therefore need separate interpretation rather than being combined into one claim about the overall effect of the strategies.

Finally, the record's gaps around missing data, analysis populations, masking, and multiplicity are relevant to reproducibility. They do not erase the posted results, but they constrain how fully those results can be evaluated. For biostatisticians and clinical researchers, the case demonstrates the value of reporting a complete analysis plan, transparent denominators, and outcome-specific uncertainty alongside the main comparison.

19. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several limitations specific to this trial affect interpretation. The registry enrollment is 66, whereas the primary outcome denominators are 33 and 32. The record does not explain the difference or state how all randomized participants were handled. Without that information, it is not possible to assess attrition or determine whether the reported analysis included every randomized participant.

The study is described as single-masked, but the masked parties and masking procedures are not reported. The registry also does not give the randomization method, allocation concealment procedures, or details of adherence to either antibiotic-use strategy. The strategies are described in broad terms, and the particular guideline criteria and score implementation are not available in the record.

The primary result is based on a relatively small set of participants and uses an outcome count, while the secondary treatment-failure comparison is also imprecise. The registry does not report the treatment-failure definition, missing-data methods, analysis population, or sensitivity analyses. These omissions make it difficult to evaluate the robustness of the estimates or the potential effect of incomplete outcome ascertainment.

The registry identifies a chi-squared method for its reported odds ratios, but the precise calculation details are not reported. The recalculated estimates use posted summary counts and are unadjusted; their confidence intervals differ from the sponsor-reported intervals. The record does not provide enough detail to resolve that difference. Finally, the registry does not report multiplicity procedures for the secondary outcome's repeated assessment times. These constraints limit replication and broader inference from the posted results.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Bacterial Pneumonia Score (BPS) Guided Antibiotic Use in Children With Pneumonia and Pneumococcal Vaccine — NCT01875731.
• Registry record last updated 2017-03-03; status last verified 2017-01.
• Results first posted 2015-05-04.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

23. Record Summary

NCT01875731 is a completed randomized, single-masked, parallel-group study sponsored by Hospital General de Niños Pedro de Elizalde. It compared Bacterial Pneumonia Score-guided antibiotic use with guideline-guided care for children with community-acquired pneumonia. Registered enrollment was 66, and the study ran from 2013-07 to a primary completion date of 2014-05.

The primary outcome counted participants with any antibiotic use after diagnosis at day 7. The registry reports an odds ratio of 5.09, a 95% confidence interval from 1.57 to 16.8, and p = 0.01. A separate unadjusted recalculation from the posted counts gives a risk difference of 0.384, with a 95% confidence interval from 0.160 to 0.608. Treatment failure was the listed secondary outcome; its comparison is imprecise. The record reports zero serious and zero other adverse events in both groups, with important details about analysis and follow-up not reported in the registry record.