Registration status and methodological reporting of randomized controlled trials in obesity research: A review
Bibliographic record
Abstract
Objective To assess registration and reporting details of randomized controlled trials (RCTs) published from 2011 to 2016 across four obesity journals. Methods All issues from four leading obesity journals were searched systematically for RCTs from January 2011 to June 2016. Data on registration status were extracted from manuscripts, online trial registries, and a trial database; corresponding authors were contacted for registration details, when necessary. The methodological reporting of RCTs was assessed on specific criteria from the Consolidated Standards of Reporting Trials. Results A total of 223 RCTs were reviewed. Three‐quarters (n = 170) were registered publicly; 94 (55.3%) reported registration details in the manuscript, and 82 (48.2%) were registered prospectively. Newer RCTs were more likely to be registered prospectively than older RCTs (2014‐2016: 57.3% vs. 2011‐2013: 39.2%; c2 = 5.5, P = 0.02). Assessment on the Consolidated Standards of Reporting Trials demonstrated that less than half of all studies reported data collection dates (n = 108; 48.4%) or included “randomized trial” in the title (n = 89; 39.9%). Conclusions The methodological reporting of RCTs published in obesity journals is suboptimal, despite current guidelines and policies. To complement existing standards, editorial boards should incorporate mandatory fields within the online manuscript submission process to enhance the quality, transparency, and comprehensiveness of reporting RCTs in obesity journals.
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 | 0.455 | 0.821 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.032 | 0.041 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".