Sex distribution in clinical trials of radiologic contrast agents: A 27-year review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Clinical trials play a pivotal role in assessing the safety and efficacy of medical therapies. Addressing sex distribution among enrollees in clinical trials of radiologic contrast agents is essential for ensuring the generalizability of trial outcomes. Previous research has highlighted the influence of demographic factors, particularly sex, on treatment responses, emphasizing the need for equitable representation in clinical trials. Our study aim was to determine the sex distribution of enrollees in clinical trials of radiologic contrast agents. METHODS: Our retrospective study included a total of 65 clinical trials conducted between 1990 and 2017 identified on clinicaltrials.gov after a comprehensive review including searching individually for all FDA approved contrast agents. Data collected included the year of FDA approval, the number of participants, sex distribution, trial location, trial phase, and study type. Inter-rater validation ensured data accuracy. RESULTS: Our analysis revealed fluctuations in sex distribution of trial enrollees. Enrollment of males exceeded females in most years, with a shift towards a more equitable representation in recent trials. Trials conducted in the United States had a higher rate of enrollment by females. Phase I trials had the most balanced representation, whereas Phase IV trials had the highest sex disparity. CONCLUSION: Across all trials, females made up 47.3 % of enrollees [3316 out of 7016 total enrollees]. Enrollment of males exceeded females in 44 of the 65 trials studied, females outnumbered males in 19 trials, and enrollment was equal between the sexes in 2 trials. While the sex distribution observed across all trials represents an equitable representation of enrollees, the wide variance of sex distribution at the level of individual trials has the potential to limit the generalizability of results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it