Reporting of Participant Demographics in Clinical Trials Published in General Radiology Journals
Bibliographic record
Abstract
OBJECTIVES: The reporting of research participant demographics provides insights into study generalizability. Our study aimed to determine the frequency at which participant age, sex/gender, race/ethnicity, and socioeconomic status (SES) are reported and used for subgroup analyses in radiology randomized controlled trials (RCTs) and their secondary analyses; as well as the study characteristics associated with, and the classification systems used for demographics reporting. METHODS: RCTs and their secondary analyses published in 8 leading radiology journals between 2013 and 2021 were included. Associations between study characteristics and demographic reporting were tested with the chi-square goodness of fit test for categorical variables, Wilcoxon-Mann-Whitney test for impact factor, and logistic regression for publication year. RESULTS: Among 432 included articles, 89.4% (386) reported age, 90.3% (390) sex/gender, 5.6% (24) race/ethnicity, and 3.0% (13) SES. Among articles that reported these demographics and were not specific to a subgroup, results were analyzed by age in 14.2% (55/386), sex/gender in 19.4% (66/340), race/ethnicity in 13.6% (3/22), and SES in 46.2% (6/13). Journal, impact factor, and last author continent were predictors of race/ethnicity and SES reporting. Funding was associated with race/ethnicity reporting. No study reported sex and gender separately, or documented transgender, nonbinary gender spectrum or intersex participants. A single category for race/ethnicity was used in 37.5% (9/24) of studies, consisting of either "White" or "Caucasian." CONCLUSION: The reporting of participant demographics in radiology trials is variable and not always representative of the population diversity. Editorial guidelines on the reporting and analysis of participant demographics could help standardize practices.
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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 | 0.416 | 0.732 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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; 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".