The effects of specific support to hypothesis generation on the diagnostic performance of medical students
Bibliographic record
Abstract
The hypothetico-deductive method, which involves an iterative process of hypothesis generation and evaluation, has been used for decades by physicians to diagnose patients. This study focuses on the levels of support that medical information systems can provide during these stages of the diagnostic reasoning process. The physician initially generates a list of possible diagnoses (hypotheses) based on the patients' symptoms. Later, those hypotheses are examined to determine which ones best account for the signs, symptoms, physical examination findings, and laboratory test results. Hypothesis generation is especially challenging for medical students because the organization of knowledge in medical school curricula is disease-centered. Furthermore, the clinical reference tools that are regularly used by medical students (such as Harrison's Online, UpToDate, and eMedicine) are mostly organized by disease. To address this issue, Abduction, a hypothesis generation tool; was developed for this study. Sixteen medical students were asked to solve two patient cases in two different conditions: A (support of clinical reference tools chosen by the participant and Abduction ) and B (support of clinical reference tools chosen by the participant). In Condition A, participants were able to generate the correct diagnosis in all 16 occasions (100%) and were able to confirm it in 13 occasions (81.25%). In Condition B, participants were able to generate the correct diagnosis in three out of 16 occasions (18.75%) and were able to confirm it once (6.25%). The implications of this study are discussed with respect to the cognitive support that Abduction can provide to medical students for clinical diagnosis.
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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 | 0.084 | 0.643 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".