Discrepancy of Female Enrolment in Stroke Secondary Prevention Studies : Relationship Between investigative Site Personnel Sex and Selection of Participants
Bibliographic record
Abstract
Background:Randomized controlled trials (RCTs) provide the strongest evidence of efficacy for medical interventions. However, a major criticism about the external validity of RCT results is the under-representation of women. Previous studies on cardiovascular disease have found female representation in secondary prevention RCTs to be less than 1/3. This underrepresentation of women can lead to suboptimal conclusions regarding their care. Currently, limited data are available regarding successful recruitment strategies for women in RCTs. It is important to understand the barriers to recruiting women in secondary prevention RCTs and to identify strategies to overcome those barriers. Objective: To determine whether an association exists between the gender of the investigative site personnel involved in the consent process and the sex distribution of participants in NAVIGATE ESUS. Methods:We plan to analyse data from 23 Canadian Navigate ESUS sites that recruited 573 participants to determine the proportion of female subjects recruited, and whether a discrepancy exists according to the gender of the primary study personnel involved in the consenting process.Conclusion: A major criticism of the external validity of RCT results is the discrepancy of female to male representation. Through investigating the discrepancies and barriers to equal sex distribution in clinical research our study will help identify factors to promote female inclusion in RCTs and pave way to future studies looking at strategies to overcome these barriers.
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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 | 0.342 | 0.572 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".