Sex Representation Among Principal Investigators in Cardiac Surgery Clinical Trials in the United States
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the sex representation among principal investigators (PIs) in US cardiac surgery clinical trials. SUMMARY BACKGROUND DATA: Being a principal investigator in a US clinical trial confers national recognition among peers. Sex representation among principal investigators (PIs) in US cardiac surgery clinical trials has not been evaluated. METHODS: We evaluated 124 US cardiac surgery trials registered on Clin-icalTrials.gov from 2014 to 2019. Sixty trials included PIs (n = 266) from 128 institutions that had a combined total of 1040 adult cardiac surgeons. We examined sex representation among junior-level (instructor or assistant professor) and senior-level (associate, full, or Emeritus professor) PIs by calculating the participation-to-prevalence ratio (PPR), whereby a PPR range of 0.8 to 1.2 reflects equitable representation. RESULTS: The pool representation percentage was 6.1% (63/1040) for women and 93.9% (977/1040) for men. A total of 266 PI positions were assigned to adult cardiac surgeons: 6 (9.5%; PPR = 0.37) from the female pool and 260 (26.6%; PPR = 1.04) from the male pool ( P = 0.004). The percentage of PIs with studies funded by industry was 9.5% ofthe female pool (PPR = 0.39) and 25.0% of the male pool (PPR = 1.04) ( P = 0.009). No National Institutes of Health-funded or other funded trials had female PIs. An overall trend was observed toward disproportionally more men than women among PIs, especially at the senior level ( P = 0.027). CONCLUSIONS: Equitable opportunities for PI positions are available for junior-level but not senior-level cardiothoracic surgeons. These results suggest a need for active engagement and promotion of equal opportunities in cardiac surgery.
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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 | 0.141 | 0.266 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".