Cohort Study of Features Used by Experts to Diagnose Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The diagnosis of transient ischemic attack (TIA) is largely dependent on a process of clinical decision-making that remains poorly characterized in the absence of a validated and accessible biomarker or imaging test. We performed a retrospective chart review to identify variables associated with a final neurologist diagnosis of TIA/stroke. METHODS: Records for all patients seen in The Ottawa Hospital's Stroke Prevention Clinic in 2015 were analyzed for patient and referral characteristics, features of the presenting neurological event, and final diagnosis by a stroke neurologist (classified as definite, possible, or definite not TIA/stroke). Multinomial logistic regression analysis with backward elimination was used to identify variables associated with the final diagnosis. RESULTS: Our inclusion criteria were met by 1894 patients. After backward elimination, 23 potentially important variables were identified, including monocular vision loss (odds ratio [OR]: 30.4, 95% confidence interval [CI]: 14.6-63.3), symptoms of sudden onset (OR: 28.3, 95% CI: 14.2-56.2), unilateral weakness affecting 2 or 3 of face, arm, or leg (OR: 17.7, 95% CI: 9.8-31.7), and homonymous hemianopia (OR: 16.6, 95% CI: 8.1-34.0). CONCLUSIONS: Accurate diagnosis of TIA is essential to initiating appropriate secondary stroke prevention therapies. A focus on elements of the patient history most commonly associated with a final diagnosis of TIA/stroke may help to identify patients in greatest need of urgent SPC assessment and allow for the provision of effective and efficient stroke prevention services.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".