Prospective Registration of Trials: Where we are, why, and how we could get better
Bibliographic record
Abstract
Objectives: Transparent trial conduct requires prospective registration of a randomized controlled trial before the enrolment of the first participant. Registration aims to minimize potential biases through unjustified or hidden modification of trial design. We aimed to (1) estimate the proportion of randomized controlled trials that are prospectively registered and determine the time trends and the factors associated with prospective registration; (2) evaluate the reasons for non-adherence with prospective registration and explore potential mechanisms to enhance adherence with prospective registration. We studied trials published in rheumatology as a case study. Design and setting: We searched for reports of trials in rheumatology published between January 2009 and December 2022 using MEDLINE-PubMed. We retrieved trial registration numbers using metadata and reviewed full texts. We conducted a multivariable logistic regression to identify factors associated with prospective trial registration. We sent an online survey to authors of trials that were not prospectively registered. We inquired about possible reasons for non-adherence with prospective registration and asked about potential solutions. Results: We identified 1093 primary reports of randomized controlled trials; 453 (41.4%) were not prospectively registered. Of these, 130 (11.9%) were not registered, and 323 (29.5%) were retrospectively registered. Prospective registration increased over time at a rate of 3% per year (p<0.001), with only 13.3% (2/15) trials prospectively registered in 2009 to 73.2% (112/153) trials in 2022. Even among journals publicly supporting ICMJE recommendation, 16% of the trials published in 2022 were not prospectively registered. In the multivariable model, prospective registration was associated with a larger sample size, recruitment conducted across countries, and publication in a journal with a higher impact factor. Trial evaluating non pharmaceutical intervention, especially education, delivery of health care or wellness, had a lower rate of prospective registration. Investigators reported lack of knowledge, or organizational problems as the main reasons for retrospective registration. Authors also suggested linking ethical approval to trial registration as the best option to ensure prospective registration. Conclusions: Despite significant improvement, adherence to prospective registration remains unsatisfactory in rheumatology. Different strategies targeting journal editors, healthcare professionals, and researchers may improve trial registration.
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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 | 0.820 | 0.924 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.035 | 0.074 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".