Panel 3: Genetics and Precision Medicine of Otitis Media
Bibliographic record
Abstract
Objective The objective is to perform a comprehensive review of the literature up to 2015 on the genetics and precision medicine relevant to otitis media. Data Sources PubMed database of the National Library of Medicine. Review Methods Two subpanels were formed comprising experts in the genetics and precision medicine of otitis media. Each of the panels reviewed the literature in their respective fields and wrote draft reviews. The reviews were shared with all panel members, and a merged draft was created. The entire panel met at the 18th International Symposium on Recent Advances in Otitis Media in June 2015 and discussed the review and refined the content. A final draft was made, circulated, and approved by the panel members. Conclusion Many genes relevant to otitis media have been identified in the last 4 years in advancing our knowledge regarding the predisposition of the middle ear mucosa to commensals and pathogens. Advances include mutant animal models and clinical studies. Many signaling pathways are involved in the predisposition of otitis media. Implications for Practice New knowledge on the genetic background relevant to otitis media forms a basis of novel potential interventions, including potential new ways to treat otitis media.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".