Sex Disparities in Spine Surgeon Leadership of Clinical Trials for Degenerative Spine Disease Research
Bibliographic record
Abstract
INTRODUCTION: Despite increasing awareness, women remain underrepresented in academic spine surgery. This study assessed whether women were equitably represented among spine surgeon PIs of clinical trials for degenerative spine disease research. METHODS: This was a retrospective cohort study of spine surgeon principal investigators (PIs) in the United States (2015 to 2022). ClinicalTrials.gov was queried for the most common diagnoses and surgeries for degenerative spine diseases. Characteristics of spine surgeon PIs were collected from academic profiles. Participation-to-prevalence ratios (PPRs) were calculated for men and women PIs relative to their prevalence among spine surgery faculty at accredited training programs. A PPR of 0.8 to 1.2 indicated equitable sex representation. A PPR <0.8 was defined as underrepresentation and >1.2 as overrepresentation. RESULTS: In total, 129 spine surgeon PIs of 91 clinical trials were included in this study. Overall, there were 125 male (97%) and four female (3%) spine surgeon PIs. Overall, women were underrepresented among spine surgeon PIs (PPR = 0.64), whereas men had equitable representation (PPR = 1.02). From 2015 to 2018, female spine surgeons were underrepresented (PPR = 0.33), but achieved equitable representation from 2019 to 2022 (PPR = 0.95). Male spine surgeons had consistently equitable representation across the study period (PPR range 1.00 to 1.03). Women had equitable representation at assistant (PPR = 1.08) and associate (PPR = 0.31) professor ranks, but were underrepresented at the full professor rank (PPR = 0). PIs were funded by industry (54%), academic institution (44%), and US Federal (2%) sources. No differences were observed in funding sources by sex ( P = 0.36). DISCUSSION: There are a limited number of female spine surgeon PIs for degenerative spine disease clinical trials, which may have negative implications on the vitality of the specialty moving forward. Future investigations are needed to understand the barriers women face in obtaining clinical trial leadership positions.
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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 | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".