Should electronic differential diagnosis support be used early or late in the diagnostic process? A multicentre experimental study of Isabel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Diagnostic errors unfortunately remain common. Electronic differential diagnostic support (EDS) systems may help, but it is unclear when and how they ought to be integrated into the diagnostic process. OBJECTIVE: To explore how much EDS improves diagnostic accuracy, and whether EDS should be used early or late in the diagnostic process. SETTING: 6 Canadian medical schools. A volunteer sample of 67 medical students, 62 residents in internal medicine or emergency medicine, and 61 practising internists or emergency medicine physicians were recruited in May through June 2020. INTERVENTION: Participants were randomised to make use of EDS either early (after the chief complaint) or late (after the complete history and physical is available) in the diagnostic process while solving each of 16 written cases. For each case, we measured the number of diagnoses proposed in the differential diagnosis and how often the correct diagnosis was present within the differential. RESULTS: EDS increased the number of diagnostic hypotheses by 2.32 (95% CI 2.10 to 2.49) when used early in the process and 0.89 (95% CI 0.69 to 1.10) when used late in the process (both p<0.001). Both early and late use of EDS increased the likelihood of the correct diagnosis being present in the differential (7% and 8%, respectively, both p<0.001). Whereas early use increased the number of diagnostic hypotheses (most notably for students and residents), late use increased the likelihood of the correct diagnosis being present in the differential regardless of one's experience level. CONCLUSIONS AND RELEVANCE: EDS increased the number of diagnostic hypotheses and the likelihood of the correct diagnosis appearing in the differential, and these effects persisted irrespective of whether EDS was used early or late in the diagnostic process.
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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 | 0.053 |
| 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) | 0.001 | 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 it