Dental caries,<i>diabetes mellitus</i>, metabolic control and diabetes duration: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To analyze articles aimed at evaluating the association between diabetes, metabolic control, diabetes duration, and dental caries. OVERVIEW: A systematic search in PubMed, Cochrane Library, Embase, and Web of Science was conducted to retrieve papers in English, Portuguese, and Spanish, up to April 2019. The research strategy was constructed considering the "PECO" strategy. Only quantitative observational studies were analyzed. The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale. The meta-analyses were performed based on random-effects models using the statistical platform R. A total of 69 articles was included in the systematic review and 40 in the meta-analysis. Type 1 diabetics have a significantly higher DMFT compared to controls. No significant differences were found between type 2 diabetics and controls and between well-controlled and poorly controlled diabetics. Concerning diabetes duration, all authors failed to find differences between groups. CONCLUSION: Although there is still a need for longitudinal studies, the meta-analysis proved that type 1 diabetics have a high dental caries risk. CLINICAL SIGNIFICANCE: It is necessary to be aware of all risk factors for dental caries that may be associated with these patients, making it possible to include them into an individualized prevention program.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".