Averting Transient Ischemic Attack Misdiagnosis : Discriminating Features From a Retrospective Chart Review
Bibliographic record
Abstract
Transient Ischemic Attack (TIA) is often used as a catchall diagnosis for patients with transient neurological events. However, stroke specialists establish a non-TIA/stroke diagnosis for up to half of Stroke Prevention Clinics patients.1-3 Arbitrary TIA diagnosis and a surplus of non-TIA referrals impedes rapid stroke services for patients truly at risk of further events. Our retrospective chart review included 1894 patients referred to The Ottawa Hospital Stroke Prevention Clinic in 2015. Descriptive statistics were used to define patient and referral characteristics, features of the presenting neurological event and final diagnosis by a stroke neurologist (classified as definitely, possibly, or definitely not TIA/stroke). Multinomial logistic regression analysis with backwards elimination and a significance level of staying in the model of u03b1 0.15 was used to identify variables associated with the final diagnosis.The final model included 20 variables. The odds of a final diagnosis of definite TIA/stroke (vs definitely not) was more than 50% lower for patients with two or more events in the past month or stereotyped features. Loss of consciousness, amnesia, lightheadedness, jerking, situational triggers, and positive visual phenomena were associated with an 83% to 98% reduced odds of final TIA/stroke diagnosis.Identification of presenting variables associated with a reduced likelihood of TIA/stroke diagnosis is important to consider when determining the provisional diagnosis for a transient neurological event. Judicious attention to features less commonly associated with TIA/stroke may influence a broader differential diagnosis, guide initial testing to enhance discrimination, and may reduce unnecessary demands for urgent stroke services.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".