Transparency in clinical trials: Adding value to paediatric dental research
Bibliographic record
Abstract
BACKGROUND: Even though considered as studies with high methodological power, many RCTs in paediatric dentistry do not have essential quality items in their design, development, and report, making results' reliability questionable, replication challenging to conduct, wasting time, money, and efforts, and even exposing the participants to research for no benefit. AIM: We addressed the main topics related to transparency in clinical research, with an emphasis in paediatric dentistry. DESIGN: We searched for all controlled clinical trials published from January 2019 up to July 2020 in the three paediatric dentistry journals with high journal Impact Factor, indexed on Medline. These papers were assessed for transparency according to Open Science practices and regarding reporting accuracy using some items required by CONSORT. RESULTS: 53.6% of the studies declared registration, 75% had sample size calculation, 98.2% reported randomisation, and from those, 65.4% explained the randomisation method. Besides that, no study shared their data, and 6.8% were published in open access format. CONCLUSIONS: Unfortunately, a large proportion of RCTs in paediatric dental research show a lack of transparency and reproducibility.
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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 | 0.749 | 0.918 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.027 | 0.032 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".