Evaluating an early childhood caries prevention strategy: a study protocol for a stepped-wedge cluster randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Caries risk assessment and management (CRAM) conducted by primary care providers is critical for preventing early childhood caries. Yet, the implementation of these preventive strategies remains understudied in low- and middle-income countries. This study aims to evaluate the implementation and effectiveness of a CRAM intervention in Chinese primary care settings. METHODS AND ANALYSIS: We will conduct a hybrid type II stepped-wedge cluster randomised controlled trial across nine primary healthcare institutions in Luzhou City, China. Institutions will be randomly allocated into three intervention waves. Primary care providers will be trained to deliver CRAM. The two primary outcomes are (1) the proportion of primary care providers completing all CRAM procedures and (2) improvements in parents' home-based oral care practices for children under age 3. Secondary outcomes include children's oral health-related quality of life, time to first caries incidence, and the cost-effectiveness of the intervention. Qualitative interviews will explore barriers and facilitators to implementation. ETHICS AND DISSEMINATION: The study protocol has received approval from the Ethics Committee of West China Hospital, Sichuan University. Study findings will be disseminated through peer-reviewed journal publications and presentations at academic conferences. Research summaries and policy briefs will be developed for key stakeholders and decision-makers at the local, provincial and national levels. TRIAL REGISTRATION NUMBER: ChiCTR2400090741.
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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 | 0.085 | 0.069 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.077 | 0.011 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".