Cardiac examination and the effect of dual-processing instruction in a cardiopulmonary simulator
Bibliographic record
Abstract
Use of dual-processing has been widely touted as a strategy to reduce diagnostic error in clinical medicine. However, this strategy has not been tested among medical trainees with complex diagnostic problems. We sought to determine whether dual-processing instruction could reduce diagnostic error across a spectrum of experience with trainees undertaking cardiac physical exam. Three experiments were conducted using a similar design to teach cardiac physical exam using a cardiopulmonary simulator. One experiment was conducted in each of three groups: experienced, intermediate and novice trainees. In all three experiments, participants were randomized to receive undirected or dual-processing verbal instruction during teaching, practice and testing phases. When tested, dual-processing instruction did not change the probability assigned to the correct diagnosis in any of the three experiments. Among intermediates, there was an apparent interaction between the diagnosis tested and the effect of dual-processing instruction. Among relative novices, dual processing instruction may have dampened the harmful effect of a bias away from the correct diagnosis. Further work is needed to define the role of dual-processing instruction to reduce cognitive error. This study suggests that it cannot be blindly applied to complex diagnostic problems such as cardiac physical exam.
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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 | 0.002 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".