Are Reports of Randomized Controlled Trials Improving over Time? A Systematic Review of 284 Articles Published in High-Impact General and Specialized Medical Journals
Bibliographic record
Abstract
BACKGROUND: Inadequate reporting undermines findings of randomized controlled trials (RCTs). This study assessed and compared articles published in high-impact general medical and specialized journals. METHODS: Reports of RCTs published in high-impact general and specialized medical journals were identified through a search of MEDLINE from January to March of 1995, 2000, 2005, and 2010. Articles that provided original data on adult patients diagnosed with chronic conditions were included in the study. Data on trial characteristics, reporting of allocation concealment, quality score, and the presence of a trial flow diagram were extracted independently by two reviewers, and discrepancies were resolved by consensus or independent adjudication. Descriptive statistics were used for quantitative variables. Comparisons between general medical and specialized journals, and trends over time were performed using Chi-square tests. RESULTS: Reports of 284 trials were analyzed. There was a significantly higher proportion of RCTs published with adequate reporting of allocation concealment (p = 0.003), presentation of a trial flow diagram (p<0.0001) and high quality scores (p = 0.038) over time. Trials published in general medical journals had higher quality scores than those in specialized journals (p = 0.001), reported adequate allocation concealment more often (p = 0.013), and presented a trial flow diagram more often (p<0.001). INTERPRETATION: We found significant improvements in reporting quality of RCTs published in high-impact factor journals over the last fifteen years. These improvements are likely attributed to concerted international efforts to improve reporting quality such as CONSORT. There is still much room for improvement, especially among specialized 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 | 0.344 | 0.737 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.049 | 0.043 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 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".