Abstract 3794: Investigating Gender Differences in Secondary Stroke Prevention Care in Ontario
Bibliographic record
Abstract
Introduction: In Ontario, more women than men (51% vs. 49% in 2009/10) are admitted for stroke or transient ischemic attack (TIA). Women are also older when they experience stroke or TIA (78 vs. 72 median years) and have strokes resulting in greater disability. Best practice stroke care indicates secondary stroke prevention is essential to reducing the likelihood of recurrent vascular events following stroke or TIA. There is currently limited information about whether or not gender differences exist in the type of care delivered at Secondary Stroke Prevention Clinics (SPCs). This study examined whether or not gender differences in the quality of care exist at SPCs across Ontario. Methods: Subjects included all consecutive stroke/TIA patients seen at 21 of 33 designated SPCs in Ontario participating in the Registry of the Canadian Stroke Network’s Stroke Performance Indicators for Reporting Improvement & Translation (SPIRIT-SPC) web-based data collection. Patients with presumed stroke/TIA referred to SPCs between January 2007 and April 2011 were identified for this study. We excluded referrals not initiated as a direct result of an acute stroke/TIA event within the past 3 months, non-strokes, and incomplete referrals/visits. We compare the performance rate for seven quality of care indicators related to secondary prevention between men and women. Results: Of the 14,966 stroke/TIA patients with at least one SPC visit, 47.2% were female and the mean age in years was 69 for women and 67 for men. Significant gender differences were found in referral reason and source, risk factors, and final diagnosis made after the first SPC visit. Women were more likely to be referred for query stroke (61.1% vs. 57.7%) and referred by an emergency or family physician (82.5% vs. 79.6%) compared to men. In terms of risk factors associated with stroke, compared to men, fewer women had a history of smoking; dyslipidemia, diabetes, and previous stroke, however, more females were obese. A higher percentage of women were diagnosed with TIA or query stroke after the first SPC visit (33.2% vs. 26.3%). Significant gender differences were observed in 3 of 7 performance indicators. Women had lower rates of neuroimaging (F=91.5%, M=92.6%, p<0.05), carotid imaging (F=87.4%, M=89.2%, p<0.05) and antithrombotics (F=91.2%, M=92.5%, p<0.05). No significant differences were found in access, time to assessment, and prescriptions for anticoagulants among patients with atrial fibrillation. Conclusion: Women are less likely to receive diagnostic imaging and antithrombotic prescriptions at or prior to a SPC visit compared to males. As best practice stroke care recommends all patients with presumed stroke/TIA should receive appropriate diagnostic evaluation and treatment, there is a need to eliminate gender gaps in secondary prevention care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".