Enhancing primary reports of randomized controlled trials: Three most common challenges and suggested solutions
Bibliographic record
Abstract
Evidence from a well-designed randomized controlled trial (RCT) is generally considered to be the gold standard that can inform clinical practice and guide decision-making. However, several deficiencies in the reporting of RCTs have frequently been identified, including incomplete, selective, and biased or inconsistent reporting. Such suboptimal reporting may lead to irreproducible results, substantial waste of resources, impaired study validity, erosion of public trust in science, and a high risk of research misconduct. In this article, we present an overview of the reporting of RCTs in the biomedical literature with a focus on the three most common reporting problems: ( i ) lack of adherence to reporting guidelines, ( ii ) inconsistencies between trial protocols or registrations and full reports, and ( iii ) inconsistencies between abstracts and their corresponding full reports. Unsatisfactory levels of adherence to guidelines and frequent inconsistencies between protocols or registrations and full reports, and between abstracts and full reports, were consistently found in various biomedical research fields. A variety of factors were found to be associated with these reporting challenges. Improved reporting can build public trust and credibility of science, save resources, and enhance the ethical integrity of research. Therefore, joint efforts from the various sectors of the biomedical community (researchers, journal editors and reviewers, educators, healthcare providers, and other research consumers) are needed to reduce and reverse the current suboptimal state of RCT reporting in the literature.
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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 | 0.649 | 0.852 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".