How Much Practice Is Enough? Using Learning Curves to Assess the Deliberate Practice of Radiograph Interpretation
Bibliographic record
Abstract
PURPOSE: To demonstrate how learning curves can describe proficiency improvements associated with deliberate practice of radiograph interpretation. METHOD: This was a prospective, cross-sectional study of pediatric residents in two tertiary care programs. A 234-item digital case bank of pediatric ankle radiographs was developed. The authors gave participants a brief clinical summary of each case and asked them to consider three radiograph views of the ankle. Participants classified each case as either normal or abnormal and, if applicable, specified the location of the abnormality. They received immediate feedback and a radiologist's dictated report. The authors reviewed longitudinal learning curves, which were generated based on calculated test characteristics (e.g., accuracy, sensitivity, specificity). RESULTS: Eighteen participants (56.3% of those eligible) completed all 234 cases. The form of the participants' learning curves was similar across all test characteristics. The curves showed a period of "noise" until the participants completed an average of 20 cases. The slope of the learning curve was maximal from 21 to 50 cases during which cumulative sensitivity (95% CI) increased from 0.50 (0.45, 0.57) to 0.54 (0.47, 0.58). Then, the curves reached an inflection point after which learning slowed but did not stop even after 234 cases. The final cumulative sensitivity was 0.60 (0.54, 0.63). Applying a reference criterion, the authors classified learners into formative categories. CONCLUSIONS: Learning curves describing deliberate practice of radiograph interpretation allow medical educators to define at which point(s) practice is most efficient and how much practice is required to achieve a defined level of mastery.
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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 | 0.016 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".