The Impact of the Middle Ear Microbiota on Otitis Media Outcomes: A Meta-Analysis of Longitudinal Studies
Bibliographic record
Abstract
Introduction: Otitis media (OM), a prevalent middle ear inflammation, often involves microbial colonization. The composition of the middle ear microbiota may influence OM outcomes, including recurrence, persistence, and treatment response. This meta-analysis investigated the relationship between the middle ear microbiota and OM outcomes. Methods: Longitudinal studies published from 2018 to 2024 that explored the middle ear microbiota and OM outcomes were systematically searched in PubMed, Embase, and Web of Science. Data on study design, participant characteristics, microbiota analysis, and OM outcomes were extracted. The risk of bias was assessed using the Newcastle-Ottawa Scale. A random-effects model was used to pool effect estimates. Results: A total of 15 studies (n = 2,540 participants) met the inclusion criteria. The middle ear microbiota diversity was significantly lower in children with recurrent OM compared to those without (standardized mean difference [SMD] = -0.45, 95% confidence interval [CI] -0.62 to -0.28, p < 0.001). The presence of specific pathogens, including Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis, was associated with an increased risk of OM recurrence (odds ratio [OR] 1.75, 95% CI 1.32 to 2.31, p < 0.001). Additionally, microbial dysbiosis was associated with delayed resolution of OM and increased antibiotic treatment failure. Conclusion: The middle ear microbiota composition significantly impacts OM outcomes. Reduced diversity and specific pathogens are associated with increased OM recurrence. These findings highlight the potential for microbiota-targeted interventions in OM management.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.055 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".