Proposed Quality Indicators for Aspects of Pediatric Acute Otitis Media Management
Bibliographic record
Abstract
BACKGROUND: The high incidence of pediatric acute otitis media (AOM) makes the implications of overdiagnosis and overtreatment far-reaching. Quality indicators (QIs) for AOM are limited, drawing from generalized upper respiratory infection QIs, or locally developed benchmarks. Recognizing this, we sought to develop pediatric AOM QIs to build a foundation for future quality improvement efforts. METHODS: Candidate indicators (CIs) were extracted from existing guidelines and position statements. The modified RAND Corporation/University of California, Los Angeles (RAND/UCLA) appropriateness methodology was used to select the final QIs by an 11-member expert panel consisting of otolaryngology-head and neck surgeons, a pediatrician and family physician. RESULTS: Twenty-seven CIs were identified after literature review, with an additional CI developed by the expert panel. After the first round of evaluations, the panel agreed on 4 CIs as appropriate QIs. After an expert panel meeting and subsequent second round of evaluations, the panel agreed on 8 final QIs as appropriate measures of high-quality care. The 8 final QIs focus on topics of antimicrobial management, specialty referral, and tympanostomy tube counseling. CONCLUSIONS: Evidence of variable and substandard care persists in the diagnosis and management of pediatric AOM despite the existence of high-quality guidelines. This study proposes 8 QIs which compliment guideline recommendations and are meant to facilitate future quality improvement initiatives that can improve patient outcomes.
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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 | 0.069 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".