Blink or Think
Bibliographic record
Abstract
PURPOSE: Experienced clinicians derive many diagnoses intuitively, because most new problems they see closely resemble problems they've seen before. The majority of these diagnoses, but not all, will be correct. This study determined whether further reflection regarding initial diagnoses improves diagnostic accuracy during a high-stakes board exam, a model for studying clinical decision making. METHOD: Keystroke response data were used from 500 residents who took the 2010 American Board of Internal Medicine (ABIM) Internal Medicine Certification Examination. Data included time to initial response on each question, whether the answer was correct, and whether or not the resident changed her or his initial response. The focus was on 80 diagnosis questions that comprised realistic clinical vignettes with multiple-choice single-best answers. Cognitive skill (ability) was measured using overall exam scores. Case complexity was determined using item difficulty (proportion of examinees that correctly answered the question). A hierarchical generalized linear model was used to assess the relationship between time spent on initial responses and the probability of correctly answering the questions. RESULTS: On average, residents changed their responses on 12% of all diagnosis questions (or 9.6 questions out of 80). Changing an answer from incorrect to correct was almost twice as likely as changing an answer from correct to incorrect. The relationship between response time and accuracy was complex. CONCLUSIONS: Further reflection appears to be beneficial to diagnostic accuracy, especially for more complex cases.
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 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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.135 | 0.083 |
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".