Caries Prevalence in Non-Syndromic Patients with Cleft Lip and/or Palate: A Meta-Analysis
Bibliographic record
Abstract
AIM: To evaluate caries prevalence in non-syndromic patients with cleft lip and/or palate (CLP) in comparison with a matched non-CLP population. METHODS: A literature search was conducted in order to identify articles reporting on the prevalence of caries in CLP versus non-CLP individuals. The related citations function in PubMed and reference lists of retrieved articles were used to expand the search. Only studies with a suitable matched control group were included. From each included study, study and sample characteristics were extracted, as were results. The main outcome was the score given for caries prevalence in each study, using a well-defined index. The data were entered into meta-analysis software and a meta-analysis performed using the random-effects model. RESULTS: From the 592 articles initially identified, 7 were chosen according to preset inclusion and exclusion criteria. All of the studies were cross-sectional in nature, and used the decayed, missing, and filled (DMF/dmf) indices as the final outcomes. The included studies involved a total of 474 CLP patients aged 1.5-29 years. When looking at permanent teeth, data from 5 studies suggest that CLP patients have a higher number of DMF teeth than the controls (mean difference 1.38; p = 0.003). For deciduous teeth, data from 4 studies suggest that CLP patients have a higher number of dmf teeth than the controls (mean difference 1.51; p = 0.03). CONCLUSION: Non-syndromic patients with CLP tend to have higher caries prevalence, both in the permanent and the deciduous dentition, in comparison with matched non-CLP controls.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.057 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".