The Risk of Bias in Randomized Trials in General Dentistry Journals
Bibliographic record
Abstract
The use of a randomized controlled trial (RCT) research design is considered the gold standard for conducting evidence-based clinical research. In this present study, we aimed to assess the quality of RCTs in dentistry and create a general foundation for evidence-based dentistry on which to perform subsequent RCTs. We conducted a systematic assessment of bias of RCTs in seven general dentistry journals published between January 2011 and March 2012. We extracted study characteristics in duplicate and assessed each trial's quality using the Cochrane Risk of Bias tool. We compared risk of bias across studies graphically. Among 1,755 studies across seven journals, we identified 67 RCTs. Many included studies were conducted in Europe (39%), with an average sample size of 358 participants. These studies included 52% female participants and the maximum follow-up period was 13 years. Overall, we found a high percentage of unclear risk of bias among included RCTs, indicating poor quality of reporting within the included studies. An overall high proportion of trials with an "unclear risk of bias" suggests the need for better quality of reporting in dentistry. As such, key concepts in dental research and future trials should focus on high-quality reporting.
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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 | 0.672 | 0.871 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".