Sex bias in referral of women to outpatient cardiac rehabilitation? A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease continues to be among the leading causes of morbidity and mortality among men and women globally. However, research suggests that women are significantly underrepresented in cardiac rehabilitation (CR), programmes which are shown to reduce recurrent cardiac events and related premature death. However, sex differences in referral rates have not been systematically and quantitatively reviewed. Hence, the objective of the study was to assess whether a significant sex difference exists. METHODS: We searched Scopus, MEDLINE, CINAHL, PsycINFO, PubMed, and The Cochrane Library databases for studies reporting CR referral rates in women and men published between July 2000 and July 2011. Titles and abstracts were screened, and the selected full-text articles were independently screened based on predefined inclusion/exclusion criteria. Included articles were assessed for quality using STROBE. RESULTS: Of 623 screened articles, 19 observational studies reporting data for 241,613 participants (80,505 women) met the inclusion criteria. In the pooled analysis, women (39.6%) were significantly less likely to be referred to CR compared to men (49.4%; odds ratio 0.68, 95% confidence interval 0.62-0.74). Heterogeneity was considered significant (I (2 )= 90%). There was no change in significant findings when subgroup analyses were conducted, examining fee for service vs. no fee, high-quality studies vs. others, or studies pooled by different study methodologies. CONCLUSIONS: CR referral remains low for all patients, but is significantly lower for women than men. Evidence-based interventions to increase referral for all patients, including women, need to be instituted. It is time to ensure broader implementation of these strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.100 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.065 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".