Adherence to guideline‐recommended care of late‐onset hypertension in females versus males: A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Sex-based disparities in cardiovascular outcomes may be improved with appropriate hypertension management. OBJECTIVE: To compare the evidence-based evaluation and management of females with late-onset hypertension compared to males in the contemporary era. METHODS: Design: Retrospective population-based cohort study. SETTING: Ontario, Canada. PARTICIPANTS: Residents aged ≥66 years with newly diagnosed hypertension between January 1, 2010, and December 31, 2017. EXPOSURE: Sex (female vs. male). OUTCOMES AND MEASURES: We used Poisson and logistic regression to estimate adjusted sex-attributable differences in the performance of guideline-recommended lab investigations. We estimated adjusted differences in time to the prescription of, and type of, first antihypertensive medication prescribed between females and males, using Cox regression. RESULTS: Among 111,410 adults (mean age 73 years, 53% female, median follow-up 6.8 years), females underwent a similar number of guideline-recommended investigations (adjusted incidence rate ratio, 0.997 [95% confidence interval [CI] 0.99-1.002]) compared to males. Females were also as likely to complete all investigations (0.70% females, 0.77% males; adjusted odds ratio, 0.96 [95% CI 0.83-1.11]). Females were slightly less likely to be prescribed medication (adjusted hazard ratio [aHR] 0.98 [95% CI 0.96-0.99]) or, among those prescribed, less likely to be prescribed first-line medication (aHR, 0.995 [95% CI 0.994-0.997]). CONCLUSIONS: Compared to males, females with late-onset hypertension were equally likely to complete initial investigations with comparable prescription rates. These findings suggest that there may be no clinically meaningful sex-based differences in the initial management of late-onset hypertension to explain sex-based disparities in cardiovascular outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".