Tympanometry interpretation by primary care physicians. A report from the International Primary Care Network (IPCN) and the Ambulatory Sentinel Practice Network (ASPN).
Bibliographic record
Abstract
BACKGROUND: The accuracy of data gathered by primary care clinicians in practice-based research networks (PBRNs) has been questioned. Tympanometry, recently recommended as a means of improving accuracy of diagnosing acute otitis media, was included as an objective diagnostic measure in an international PBRN study. We report the level of agreement of interpretations of tympanograms between primary care physicians in PBRNs and experts. METHODS: Primary care physicians in PBRNs in the Netherlands, United Kingdom, United States, and Canada enrolled 1773 children aged 6 to 180 months who contributed 6358 tympanograms during 3179 visits. The physicians were trained in the use and interpretation of tympanometry using the Modified Jerger Classification. We determined the level of agreement between physicians and experts for interpretation of tympanograms. One comparison used the 6358 individual ear tracings. A second comparison used the 3179 office visits by children as the unit of analysis. RESULTS: The distribution of expert interpretation of all tympanograms was: 35.8% A, 30% B, 15.5% C1, 12% C2, and 6.8% uninterpretable; for visits, 37.8% were normal (A or C1), 55.6% abnormal (B or C2), and 6.6% could not be classified. There was a high degree of agreement in the interpretation of tympanograms between experts and primary care physicians across networks (kappa=0.70-0.77), age groups of children (kappa=0.69-0.73), and types of visits (kappa=0.66-0.77). This high degree of agreement was also found when children were used as a unit of analysis. CONCLUSIONS: Interpretations of tympanograms by primary care physicians using the Modified Jerger Classification can be used with confidence. These results provide further evidence that practicing primary care physicians can provide high-quality data for research purposes.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".