Disrupting Diagnostic Reasoning
Bibliographic record
Abstract
PURPOSE: Others have suggested that increased time pressure, sometimes caused by interruptions, may result in increased diagnostic errors. The authors previously found, however, that increased time pressure alone does not result in increased errors, but they did not test the effect of interruptions. It is unclear whether experience modulates the combined effects of time pressure and interruptions. This study investigated whether increased time pressure, interruptions, and experience level affect diagnostic accuracy and response time. METHOD: In October 2012, 152 residents were recruited at five Medical Council of Canada Qualifying Examination Part II test sites. Forty-six emergency physicians were recruited from one Canadian and one U.S. academic health center. Participants diagnosed 20 written general medicine cases. They were randomly assigned to receive fast (time pressure) or slow condition instructions. Visual and auditory case interruptions were manipulated as a within-subject factor. RESULTS: Diagnostic accuracy was not affected by interruptions or time pressure but was related to experience level: Emergency physicians were more accurate (71%) than residents (43%) (F = 234.0, P < .0001) and responded more quickly (54 seconds) than residents (65 seconds) (F = 9.0, P < .005). Response time was shorter for participants in the fast condition (55 seconds) than in the slow condition (73 seconds) (F = 22.2, P < .0001). Interruptions added about 8 seconds to response time. CONCLUSIONS: Experienced emergency physicians were both faster and more accurate than residents. Instructions to proceed quickly and interruptions had a small effect on response time but no effect on accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.454 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".