Referral of children with otitis media. Do family physicians and pediatricians agree?
Bibliographic record
Abstract
OBJECTIVE: To determine factors influencing family physicians' and pediatricians' decisions to refer children with recurrent acute otitis media (RAOM) and otitis media with effusion (OME) to otolaryngologists for an opinion about tympanostomy tube insertion. DESIGN: Mailed survey. SETTING: Physicians' practices in Ontario. PARTICIPANTS: Random sample of 1459 family physicians and all 775 pediatricians in the province. MAIN OUTCOME MEASURES: Physicians' reports of the influence of 17 factors on decisions to refer (more likely, no influence, less likely to refer) and number of episodes of otitis media, months with effusion, level of hearing loss, or months of continuous antibiotics without improvement prompting referral. RESULTS: Physicians agreed (> 80% concordance) on six out of 17 factors as indications for referring children with RAOM or OME. Opinions about the importance of other factors varied widely. Family physicians would refer children with otitis media after fewer episodes of illness, fewer months of effusion, lower levels of hearing loss, and fewer months of prophylactic antibiotic therapy than pediatricians (all P < .001). Pediatricians would prescribe continuous antibiotics longer (11.8 weeks) than family physicians (8.9 weeks, P < .0001), which correlated with lower referral thresholds for family physicians. CONCLUSION: Family physicians' and pediatricians' self-reported referral practices for surgical opinions on children with otitis media varied considerably. These observations raise questions about the consistency of care for children with otitis media and whether revised clinical guidelines would be helpful.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".