Computer-assisted instruction of carpal bone radiograph interpretation
Bibliographic record
Abstract
This paper describes the development of a computer-assisted self-directed learning module to teach carpal bone radiograph interpretation to clinical clerks, and the evaluation of its efficacy as a teaching tool. The module was developed using commercially available authoring software and in collaboration with radiologists. Following an eight-month period where the module was a mandatory component of the clerkship rotation in emergency medicine, a convenience sample of 36 volunteers from various levels of medical training were recruited, and their ability to interpret carpal radiographs was tested in an objective structured clinical examination (OSCE). The scores of the clinical clerks who had completed the module were shown to be similar to the scores of emergency medicine residents with specific experience and training in this skill (mean: 5 vs. 6), and different from the scores of trainees who had not completed the module and had no specific training in this skill (mean: 5 vs. 2). This study suggests that our module may be an effective tool in teaching this skill to clinical clerks.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".