Diagnosis of Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Research suggests that women and men may present with different transient ischemic attack (TIA) and stroke symptoms. We aimed to explore symptoms and features associated with a definite TIA/stroke diagnosis and whether those associations differed by sex. METHODS: We completed a retrospective cohort study of patients referred to The Ottawa Hospital Stroke Prevention Clinic in 2015. Exploratory multinomial logistic regression was used to evaluate candidate variables associated with diagnosis and patient sex. Backwards elimination of the interaction terms with a significance level for staying in the model of 0.25 was used to arrive at a more parsimonious model. RESULTS: Based on 1770 complete patient records, sex-specific differences were noted in TIA/stroke diagnosis based on features such as duration of event, suddenness of symptom onset, unilateral sensory loss, and pain. CONCLUSIONS: This preliminary work identified sex-specific differences in the final diagnosis of TIA/stroke based on common presenting symptoms/features. More research is needed to understand if there are biases or sex-based differences in TIA/stroke manifestations and diagnosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.010 | 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".