Quality of cluster randomized controlled trials in oral health: a systematic review of reports published between 2005 and 2009
Bibliographic record
Abstract
OBJECTIVES: To assess the quality of methods and reporting of recently published cluster randomized trials (CRTs) in oral health. METHODS: We searched PubMed for CRTs that included at least one oral health-related outcome and were published from 2005 to 2009 inclusive. We developed a list of criteria for assessing trial quality and reporting. This was influenced largely by the extended CONSORT statement for CRTs but also included criteria suggested by other authors. We examined the extent to which trials were consistent with these criteria. RESULTS: Twenty-three trials were included in the review. In 15 (65%) trials, clustering had been accounted for in sample size calculations, and in 18 (78%) authors had accounted for clustering in analysis. Intraclass correlation coefficients (ICCs) were reported for eight (35%) trials; the outcome assessor was reported as having been blinded to allocation in 12 (52%) trials; 17 (74%) described eligibility criteria at individual level, but only nine (39%) described such criteria at cluster level. Sixteen of 20 trials (80%), in which individuals were recruited, reported that individual informed consent was obtained. CONCLUSIONS: These results suggest that the quality of recent CRTs in oral health is relatively high and appears to compare favourably with other fields. However, there remains room for improvement. Authors of future trials should endeavour to ensure sample size calculations and analyses properly account for clustering (and are reported as such), consider the potential for recruitment/identification bias at the design stage, describe the steps taken to avoid this in the final report and report observed ICCs and cluster-level eligibility criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.946 | 0.905 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.239 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".