How do neurologists diagnose transient ischemic attack: A systematic review
Bibliographic record
Abstract
BACKGROUND: Identifying and treating patients with transient ischemic attack is an effective means of preventing stroke. However, making this diagnosis can be challenging, and over a third of patients referred to stroke prevention clinic are ultimately found to have alternate diagnoses. AIMS: We performed a systematic review to determine how neurologists diagnose transient ischemic attack. SUMMARY OF REVIEW: A systematic literature search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines using MEDLINE, Embase, and the Cochrane Library databases. Publications eligible for inclusion were those that included information on the demographic or clinical features neurologists use to diagnose transient ischemic attacks or transient ischemic attack-mimics. Of 1666 citations, 210 abstracts were selected for full-text screening and 80 publications were ultimately deemed eligible for inclusion. Neurologists were more likely to diagnose transient ischemic attack based on clinical features including negative symptoms or speech deficits. Patients with positive symptoms, altered level of consciousness, or the presence of nonfocal symptoms such as confusion or amnesia were more likely to be diagnosed with transient ischemic attack-mimic. Neurologists commonly include mode of onset (i.e. sudden versus gradual), recurrence of attacks, and localizability of symptoms to a distinct vascular territory in the diagnostic decision-making process. Transient ischemic attack diagnosis was more commonly associated with advanced age, preexisting hypertension, atrial fibrillation, and other vascular risk factors. CONCLUSIONS: Neurologists rely on certain clinical and demographic features to distinguish transient ischemic attacks from mimics, which are not currently reflected in widely used risk scores. Clarifying how neurologists diagnose transient ischemic attack may help frontline clinicians to better select patients for referral to stroke prevention clinics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".