Reporting of patient consent in healthcare cluster randomised trials is associated with the type of study interventions and publication characteristics
Bibliographic record
Abstract
OBJECTIVE: Cluster randomised trial (CRT) investigators face challenges in seeking informed consent from individual patients (cluster members). This study examined associations between reporting of patient consent in healthcare CRTs and characteristics of these trials. STUDY DESIGN: Consent practices and study characteristics were abstracted from a random sample of 160 CRTs performed in primary or hospital care settings that were published from 2000 to 2008. Multivariable logistic regression was used to examine associations between reporting of patient consent and methodological characteristics, as well as publication features such as date and journal of publication. RESULTS: 82 (53.8%) of 160 studies reported obtaining informed consent from individual patients. Reporting of patient consent was independently and positively associated with: smaller cluster size, the evaluation of experimental interventions targeted at patients, data collection from individual patients, publication later than 2004 and publication in higher-impact journals. CONCLUSIONS: Reporting of consent practices in published CRTs should be improved. Consent practices in published CRTs appear to be related to the type of interventions under study, as well as journal impact and trends in research ethics practices. These findings will inform best practices in trial conduct and ethics review, remediation of errors in consent practices and ethics review and the development of regulatory guidance for CRTs.
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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 | 0.673 | 0.879 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".