Abstract TP393: Sex Differences in Neurologist Diagnosis of Transient Ischemic Attack in Association With Presenting Symptoms
Bibliographic record
Abstract
Effective management of Transient Ischemic Attack (TIA) and stroke hinges on accurate diagnosis. Research suggests that women and men present with different TIA/stroke symptoms, and those more commonly reported by women are often considered atypical, or benign. For this reason, TIA or stroke in women may be underdiagnosed and undertreated. Growing evidence that women are disproportionately affected by stroke underpins this study focused on neurologist diagnosis of stroke/TIA in association with presenting symptoms. A retrospective chart review was performed using data from the charts of all patients referred to The Ottawa Hospital Stroke Prevention Clinic in 2015 with a provisional diagnosis of TIA/stroke. Demographics, event characteristics, and final diagnosis were extracted from each chart. Multinomial logistic regression analysis with backwards elimination and a significance level staying in the model of α=0.15 was used to identify variables associated with a final diagnosis of definite TIA/stroke. A sub-analysis was completed for the final model by sex. Of the 1864 included patients, 932 (50%) were women. There were no significant differences in age or final diagnosis of definite TIA/stroke based on sex. Among patients reporting classic stroke symptoms such as unilateral weakness, aphasia, and amaurosis fugax both sexes demonstrated significantly higher odds of a final diagnosis of TIA/stroke; however the odds were higher for men than women. For example, men with unilateral weakness, aphasia or amaurosis fugax had an odds ratio (OR) of 32.7, 7.7, and 27.8 respectively of having a final diagnosis of TIA/stroke, whereas the OR for women was 10.9, 5.4, and 22.2 respectively, although the 95% confidence intervals overlapped. In contrast, women reporting homonymous hemianopsia or any combination of ataxia, diplopia, or vertigo had a higher odds than men of being diagnosed with TIA/stroke (OR 21 and 8.5 respectively for women and OR 11.4 and 5.1 respectively for men). The trend in differences noted for the odds of definite TIA/stroke diagnosis among men and women may imply that women are less likely to be diagnosed with TIA even when reporting classical symptoms. More research is needed to understand sex-based differences in the diagnosis of TIA/stroke.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".