Post—Tympanostomy Tube Otorrhea: A Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: Post-tympanostomy tube otorrhea is the most common complication of tympanostomy tube placement. The incidence of this problem varies from 3.4% to 74%. Trials that study post-tympanostomy tube otorrhea may involve valid randomization "by patient" or "by ear." In an attempt to define "best practice," we conduct a meta-analysis to quantify the benefit of using topical prophylactic antibiotic drops in the postoperative period. We then compare our findings with previous results found in the literature. METHODS: We selected randomized studies for which antibiotic drops had been used for at least 48 hours after tympanostomy tube insertion. Nine studies, 3 "by ear" and 6 "by patient," met our inclusion criteria. The odds ratio and 95% confidence intervals were calculated for each to conduct the meta-analysis. RESULTS: Overall, prophylaxis appears to be effective at reducing the incidence of post-tympanostomy tube otorrhea. The odds ratios for all studies were less than 1.0. However, none of the 3 "by ear" studies and only 3 of the 6 "by patient" studies were statistically significant. The mean odds ratio was 52%, suggesting that prophylaxis may reduce the incidence of post-tympanostomy tube otorrhea by half. CONCLUSION: This meta-analysis suggests that routine post-tympanostomy tube prophylaxis is beneficial, but this finding is dependent on selection criteria used. EBM RATING: A-1a.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.046 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".