Reporting of sociodemographic characteristics of trial participants in infectious diseases clinical trials—a systematic review
Bibliographic record
Abstract
BACKGROUND: Reporting of demographic characteristics in randomized clinical trials (RCTs) is recommended to facilitate assessment of generalizability to other populations. However, there is a lack of consensus as to what variables should be reported, and there are limited data describing current research practice. OBJECTIVES: We aimed to evaluate reporting of sociodemographic characteristics of participants in infectious diseases RCTs and identify gaps in current practice. METHODS: We conducted a systematic review of all infectious diseases-related RCTs published between January 2014 and August 2023 in ten selected high-impact journals by searching the Ovid MEDLINE database. Outcomes of interest were the reporting of five patient-level sociodemographic characteristics, as recommended by the CONSORT-Equity 2017 extension to the CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines: (a) ethnicity, (b) sex and/or gender, (c) education level, (d) socioeconomic status (SES), and (e) rurality. We summarized descriptive results for the reporting of each characteristic overall, by trial type (health equity-related vs. non-health equity-related), subject area, and year of publication. We fitted multivariable logistic regression models to identify trial characteristics associated with the reporting of each characteristic. Risk of bias of trials was not assessed as our objective was to assess trial reporting and not results. RESULTS: We screened 4234 articles and included 1343. Almost all trials (1201/1233, 97.4%) reported sex and/or gender. In contrast, less than half (654/1326, 49.3%) reported ethnicity, and only a minority reported education level (113/1252, 9.0%), SES (120/1340, 9.0%), and rurality (45/1269, 3.9%). There was no improvement in reporting of each characteristic over the 10-year period. Subject area, funding source, whether a trial was health equity-related, use of a medical writer, and trial setting (high vs. low/middle-income country) were significantly associated with the reporting of ethnicity, education level, and SES. CONCLUSIONS: Reporting of sociodemographic characteristics in infectious diseases RCTs is inconsistent and has not improved over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.093 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".