Abstract TMP91: How Reliably Do Clinicians Predict Stroke Outcomes? Results from the JURaSSiC (Clinician JUdgment vs. Risk Score to predict Stroke outComes) randomized trial.
Bibliographic record
Abstract
Background: Limited information is available evaluating the accuracy of clinician judgment compared with predictive stroke outcome scores. Objectives: To compare the accuracies of clinician judgment and a validated stroke risk score (iScore) for predicting stroke patient outcomes. Methods: A convenience sample of 111 practicing clinicians (general and stroke neurologists, internists, and ER physicians) predicted the outcomes of 5 stroke patients based on case summaries. Cases were randomly selected as being representative of the 10 most common clinical scenarios (n=1,415) from a pool of over 12,000 patients admitted to stroke centers in Ontario, Canada. Stroke cases had known clinical presentation, comorbidities, stroke severity, and outcomes. Main outcomes: 30 day mortality and/or disability at discharge. Results: Evaluators’ mean age was 40±12 years; 55 (50%) were active staff physicians, 47 (42%) neurologists and 8 (7%) board certified stroke neurologists. The mean number of stroke patients assessed per physician/yr was 98 (±150); 92 (82%) provide acute stroke care (initial 48 hrs). Although on average clinicians were able to estimate stroke patient outcomes accurately (mean absolute difference for death or disability at discharge: 12.8%; 95%CI 9.3%-16.5%) ,there was significant variability in the clinicians’ predictions (Figure A). Specifically, 70-100% of clinicians’ estimates were outside the 95%CI of observed outcomes (Figure B). In contrast, 90% of the iScore-based estimates were within the 95%CI of observed outcomes. Clinicians indicated a low level of confidence (mean 39%) in estimating outcomes. Conclusions: Clinicians with expertise in stroke care made predictions for actual outcomes outside of the 95%CI in 70-100% of cases whereas predictions using the iScore fell outside the 95%CI in less than 10% of cases. The iScore may provide a useful tool to help clinicians gauge a stroke patient’s likely outcome. ClinicalTrials.gov NCT01657279
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".