Integrating Sex and Gender in Studies of Cardiac Resynchronization Therapy: A Systematic Review
Bibliographic record
Abstract
AIMS: To examine the prevalence, temporal changes, and impact of the National Institute of Health (NIH) Sex as a Biological Variable (SABV) policy on sex and gender reporting and analysis in cardiac resynchronization therapy (CRT) cohort studies. METHODS AND RESULTS: We searched MEDLINE, EMBASE, and Web of Science for cohort studies reporting the effectiveness and safety of CRT in heart failure patients from January 2000 to June 2020, with no language restrictions. Segmented regression analysis was used for policy analysis. We included 253 studies. Fourteen per cent considered sex in the study design. Outcome data disaggregated by sex were only reported in 17% of the studies. Of the studies with statistical models (n = 173), 57% were adjusted for sex. Sixty-eight per cent of those reported an effect size for sex on the outcome. Sex-stratified analyses were conducted in 13% of the studies. Temporal analysis shows an increase in sex reporting in background, statistical models, study design, and discussion. Besides statistical models, NIH SABV policy analysis showed no significant change in the reporting of sex in study sections. Gender was not reported or analysed in any study. CONCLUSIONS: There is a need to improve the study design, analysis, and completeness of reporting of sex in CRT cohort studies. Inadequate sex integration in study design and analysis may potentially hinder progress in understanding sex disparities in CRT. Deficiencies in the integration of sex in studies could be overcome by implementing guidance that already exists.
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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 | 0.036 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".