Hormone replacement therapy and ischemic stroke severity in women
Bibliographic record
Abstract
OBJECTIVE: To investigate whether ischemic stroke severity differed among women who were receiving hormone replacement therapy (HRT) as compared with those who were not receiving these drugs. BACKGROUND: Estrogen has a neuroprotective effect in animal models of ischemic stroke, but data reflecting the impact of HRT on ischemic stroke severity in humans are lacking. METHODS: All women receiving HRT at the time of admission for acute ischemic stroke to an academic medical center over 3 years were identified by medical record review (n = 58). HRT users were matched with 116 HRT nonusers by age and number of stroke risk factors. Stroke severity was assessed retrospectively with the Canadian Neurological SCALE: Data were analyzed with nonparametric univariate tests (Spearman rank and chi(2) tests) and linear regression modeling using nonparametric matched-pair analysis. RESULTS: History of congestive heart failure or coronary artery disease (p = 0.01), atrial fibrillation (p = 0.02), and African American race (p = 0.04), were significantly associated with greater stroke severity in the univariate analysis. There was a nonsignificant trend toward lesser stroke severity in HRT users (median Canadian Neurological Scale score, 10, vs 9.5 in non-HRT users, p = 0.08). Multivariate analysis showed no independent effect of HRT use on stroke severity (F = 1.24, p = 0.17). CONCLUSIONS: There was no significant effect of HRT status on stroke severity. Because this was a retrospective analysis, prospective studies are also needed to further elucidate any potential neuroprotective effect of hormone replacement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".