P.162 Neurosurgical trialists are not as diverse as the participants they enroll: A systematic sampling review
Bibliographic record
Abstract
Background: Diversity of healthcare personnel has been associated with improved care of diverse populations. To determine whether neurosurgical clinical trialists were as diverse as the populations they treated, we investigated the sex/gender and race/ethnicity of participants and compared them to authors of randomized controlled trials (RCTs) in neurosurgery, orthopedic surgery, general surgery and plastic surgery. Methods: Embase and MEDLINE were systematically searched from 2001 to 2021. RCTs were limited by impact factor and selected using a series technique. Data on author and trial characteristics were extracted independently and in duplicate, and compared for each speciality. Results: 1548 articles were included. Interim analysis revealed the mean proportion of women authors was lowest in neurosurgery (5%) and highest in plastic surgery (50%). Trialists that were most reflective of their participants sex/gender were general surgery (42% authors vs 46% participants) and plastic surgery (50% authors vs 66% participants). 94% of RCTs did not report participants’ race/ethnicity. No RCTs excluded participants based on sex/gender or race/ethnicity. Conclusions: Compared to other surgical fields, neurosurgery had the poorest correlation of author sex/gender with the population being studied. Efforts are needed to improve the diversity of neurosurgical trialists, access to RCTs for underrepresented groups and standardized reporting of participants’ race/ethnicity.
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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 | 0.039 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".