Validation of a multivariate clinical prediction model for the diagnosis of mild stroke/transient ischemic attack in physician first-contact patient settings
Bibliographic record
Abstract
We validate our previously developed (DOI: 10.1101/089227) clinical prediction rule for diagnosing transient ischemic attack on the basis of presenting clinical symptoms and compare its performance with the ABCD2 score in first-contact patient settings. Two independent and prospectively collected patient validation cohorts were used: (a) referral cohort–prospectively referred emergency department and general practitioner patients ( N = 877); and (b) SpecTRA cohort–participants recruited as part of the SpecTRA biomarker project ( N = 545). Outcome measure consisted of imaging-confirmed clinical diagnosis of mild stroke/transient ischemic attack. Results showed that our clinical prediction rule demonstrated significantly higher accuracy than the ABCD2 score for both the referral cohort (70.5% vs 59.0%; p < 0.001) and SpecTRA cohort (72.8% vs 68.3%; p = 0.028). We discuss the potential of our clinical prediction rule to replace the use of the ABCD2 score in the triage of transient ischemic attack clinic referrals.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".