Antibiotic Therapy to Prevent the Development of Asymptomatic Middle Ear Effusion in Children With Acute Otitis Media
Bibliographic record
Abstract
OBJECTIVES: To determine predictors of the development of asymptomatic middle ear effusion (MEE) in children with acute otitis media (AOM) and to assess the effect of antibiotic therapy in preventing the development of MEE in these children. DATA SOURCES: A systematic literature search was performed using PubMed, EMBASE, the Cochrane databases, and the proceedings of international otitis media symposia. STUDY SELECTION: A trial was selected if the allocation of participants to treatment was randomized, children aged 0 to 12 years with AOM were included, the comparison was between antibiotic therapy and placebo or no (antibiotic) treatment, and MEE at 1 month was measured. DATA EXTRACTION: Data from 5 randomized controlled trials were included in the meta-analysis of individual patient data (1328 children aged 6 months to 12 years). We identified independent predictors of the development of asymptomatic MEE and studied whether these children benefited more from antibiotic therapy than children with a lower risk. The primary outcome was MEE (defined as a type B tympanogram) at 1 month. DATA SYNTHESIS: The overall relative risk of antibiotic therapy in preventing the development of asymptomatic MEE after 1 month was 0.9 (95% confidence interval, 0.8-1.0; P =.19). Independent predictors of the development of asymptomatic MEE were age younger than 2 years and recurrent AOM. No statistically significant interaction effects with treatment were found. CONCLUSION: Because of a marginal effect of antibiotic therapy on the development of asymptomatic MEE and the known negative effects of prescribing antibiotics, including the development of antibiotic resistance and adverse effects, we do not recommend prescribing antibiotics to prevent MEE.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".