Should learners reason one step at a time? A randomised trial of two diagnostic scheme designs
Bibliographic record
Abstract
CONTEXT: Making a diagnosis can be difficult for learners as they must integrate multiple clinical variables. Diagnostic schemes can help learners with this complex task. A diagnostic scheme is an algorithm that organises possible diagnoses by assigning signs or symptoms (e.g. systolic murmur) to groups of similar diagnoses (e.g. aortic stenosis and aortic sclerosis) and provides distinguishing features to help discriminate between similar diagnoses (e.g. carotid pulse). The current literature does not identify whether scheme layouts should guide learners to reason one step at a time in a terminally branching scheme or weigh multiple variables simultaneously in a hybrid scheme. We compared diagnostic accuracy, perceptual errors and cognitive load using two scheme layouts for cardiac auscultation. METHODS: Focused on the task of identifying murmurs on Harvey, a cardiopulmonary simulator, 86 internal medicine residents used two scheme layouts. The terminally branching scheme organised the information into single variable decisions. The hybrid scheme combined single variable decisions with a chart integrating multiple distinguishing features. Using a crossover design, participants completed one set of murmurs (diastolic or systolic) with either the terminally branching or the hybrid scheme. The second set of murmurs was completed with the other scheme. A repeated measures manova was performed to compare diagnostic accuracy, perceptual errors and cognitive load between the scheme layouts. RESULTS: = 5.1, p = 0.003). Use of a terminally branching scheme was associated with increased diagnostic accuracy (65 versus 53%, p = 0.02), fewer perceptual errors (0.61 versus 0.98 errors, p = 0.001) and lower cognitive load (3.1 versus 3.5/7, p = 0.023). CONCLUSIONS: The terminally branching scheme was associated with improved diagnostic accuracy, fewer perceptual errors and lower cognitive load, suggesting that terminally branching schemes are effective for improving diagnostic accuracy. These findings can inform the design of schemes and other clinical decision aids.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".