Pneumatic video-otoscopy teaching improves the diagnostic accuracy of otitis media with effusion: results of a randomized controlled trial.
Bibliographic record
Abstract
BACKGROUND: the diagnostic accuracy of otitis media with effusion (OME) has been shown to be poor among medical students, residents, and practicing physicians. OBJECTIVE: to determine if the use of pneumatic video-otoendoscopic examination (VOE) improves the diagnostic accuracy of OME among residents. METHODS: pediatric residents were randomized into a "pneumatic" examination group (intervention) and a "still" examination group (control). The control group viewed a set of 25 still VOE images of the tympanic membranes of both normal and OME ears. The intervention group viewed the same still images but with the addition of pneumatic VOE assessments. Each resident documented each of his or her diagnoses as normal or OME. The accuracy of assessment for both the static and the pneumatic methods was compared. RESULTS: twenty-nine pediatric residents participated in this study: 15 in the intervention group and 14 in the control group. The overall diagnostic accuracy was 91% for the intervention group versus 78% for the control group (p = .0003). CONCLUSION: pneumatic video-otoscopy teaching improves the diagnostic accuracy of OME among residents.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".