A Performance-Based Competency Assessment of Pediatric Chest Radiograph Interpretation Among Practicing Physicians
Bibliographic record
Abstract
INTRODUCTION: There is limited knowledge on pediatric chest radiograph (pCXR) interpretation skill among practicing physicians. We systematically determined baseline interpretation skill, the number of pCXR cases physicians required complete to achieve a performance benchmark, and which diagnoses posed the greatest diagnostic challenge. METHODS: Physicians interpreted 434 pCXR cases via a web-based platform until they achieved a performance benchmark of 85% accuracy, sensitivity, and specificity. Interpretation difficulty scores for each case were derived by applying one-parameter item response theory to participant data. We compared interpretation difficulty scores across diagnostic categories and described the diagnoses of the 30% most difficult-to-interpret cases. RESULTS: 240 physicians who practice in one of three geographic areas interpreted cases, yielding 56,833 pCXR case interpretations. The initial diagnostic performance (first 50 cases) of our participants demonstrated an accuracy of 68.9%, sensitivity of 69.4%, and a specificity of 68.4%. The median number of cases completed to achieve the performance benchmark was 102 (interquartile range 69, 176; min, max, 54, 431). Among the 30% most difficult-to-interpret cases, 39.2% were normal pCXR and 32.3% were cases of lobar pneumonia. Cases with a single trauma-related imaging finding, cardiac, hilar, and diaphragmatic pathologies were also among the most challenging. DISCUSSION: At baseline, practicing physicians misdiagnosed about one-third of pCXR and there was up to an eight-fold difference between participants in number of cases completed to achieve the standardized performance benchmark. We also identified the diagnoses with the greatest potential for educational intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".