A hinting strategy for online learning of radiograph interpretation by medical students
Bibliographic record
Abstract
CONTEXT: We examined whether a 'hint' manoeuvre increases the time novice medical learners spend on reviewing a radiograph, thereby potentially increasing their interpretation accuracy. METHODS: Senior year medical students were recruited into a randomised control, three-arm, multicentre trial. Students reviewed an online 50-case learning set that varied in degree of 'hint' intervention. The 'hint' was a dialogue box that appeared after a student submitted an answer, encouraging the student to re-evaluate their interpretation. The students in the control group received no hints. In the weak intervention group, students received 'hints' with 66% of their incorrect interpretations and 33% of those that were correct. In the strong intervention group, the incorrect interpretation hint frequency was 80%, whereas for correct responses it was 20%. All students completed a 20-case post-test immediately and 2 weeks after the 50 cases. The primary outcome was student performance on the immediate post-test, measured as the ability to discriminate between normal and abnormal films (dPrime). Secondary outcomes included the probability of considering the hint, time spent on learning cases and knowledge retention at 2 weeks. RESULTS: We enrolled 117 medical students from three sites into the three study groups: control (36), weak intervention (40) and strong intervention (41) groups. The mean (standard deviation) dPrime in the control, weak and strong groups were 0.4 (1.1), 0.7 (1.1) and 0.4 (0.9), respectively (P = 0.4). In the weak and strong groups, participants reconsidered answers in 556 of 1944 (28.6%) hinting opportunities, and those who reconsidered their answers spent a mean (95% confidence interval) of 13.9 (11.9, 16.0) seconds longer on each case. There were no significant differences in knowledge retention at 2 weeks between the groups (P = 0.2). CONCLUSIONS: Although the implemented hinting strategy did result in students spending more time considering a proportion of the cases, overall it was not effective in improving student performance.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".