A Cross-sectional literature survey showed the reporting quality of multicenter randomized controlled trials should be improved
Bibliographic record
Abstract
OBJECTIVE: To assess the reporting quality of randomized controlled trials (RCTs) with multicenter design, particularly whether necessary information related to multicenter characteristics was adequately reported. STUDY DESIGN AND SETTING: Through a search of 4 international electronic databases, we identified multicenter RCTs published in English from 1975 to 2019. Reporting quality was assessed by the CONSORT (Consolidated Standards of Reporting Trials) checklist (37 items) and by a self-designed multicenter-specific checklist (27 items covering multicenter design, implement and analysis). The scores of trials published in three time periods (1975-1995; 1996-2009; and 2010-2019) were also compared. RESULTS: A total of 2,844 multicenter RCTs were included. For the CONSORT checklist, the mean (standard deviation) reporting score was 24.1 (5.5), 12 items were assessed as excellent (>90%), 12 items as good (50%-90%), and 13 items as poor (<50%). For the multicenter checklist, the reporting score was 3.9 (2.2), only 3 items were excellent or good, and the remaining 24 items were poor. Time period comparison showed that reporting quality improved over time, especially after the CONSORT 2010 issued. CONCLUSION: Although CONSORT appears to have enhanced the reporting quality of multicenter RCTs, further improvement is needed. A "CONSORT extension for multicenter trials" should be developed.
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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 | 0.618 | 0.873 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".