A Systematic Review of Sex-Specific Reporting in Heart Failure Clinical Trials
Bibliographic record
Abstract
Females are historically underenrolled in heart failure (HF) randomized controlled trials (RCTs) relative to disease prevalence. Sex differences in trial flow, including withdrawals and losses to follow up, may further limit the generalizability of results. This study aimed to assess the frequency of sex-specific reporting of trial flow, treatment efficacy, and adverse events in HF RCTs. We systematically searched MEDLINE, Embase, and CINAHL for HF RCTs published between 2000 and 2020 in journals with an impact factor ≥10. We assessed whether trial flow, treatment effect, and adverse events were disaggregated by sex. We used multivariable regression to assess associations between trial characteristics and sex subgroup analysis. We analyzed temporal trends in sex-specific reporting. We included 224 RCTs with 228,801 total participants (28.2% female). No RCT reported sex-disaggregated screening, consent, or withdrawal rates; and 2 (0.9%) reported sex-disaggregated losses to follow-up. Seventy-five RCTs (33.4%) presented sex subgroup analysis, and 63 (28.3%) reported sex-treatment interaction. No RCT reported sex-specific adverse events. Large trial size (odds ratio: 13.16, 95% CI: 5.67-30.52; P < 0.001) and device/procedure interventions (odds ratio: 5.13, 95% CI: 1.55-16.95; P < 0.007) were independently associated with sex subgroup analysis. Over the study period, there was an increase in sex subgroup analysis (P < 0.001) and testing for sex-treatment interaction (P < 0.001). HF RCTs rarely reported sex differences in trial flow or adverse events and uncommonly performed sex subgroup analysis. Improved sex-disaggregated reporting could highlight the causes and extent of sex differences in trial participation and facilitate appropriate inferences about treatment effect.
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 | 0.138 | 0.492 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".