A systematic review of the caries prevalence among children living in Chernobyl fallout countries
Bibliographic record
Abstract
The present study analyzed the data concerning the caries prevalence in children born and permanently residing in Chernobyl fallout areas. Setting forth to evaluate if differences regarding the caries prevalence can be observed compared to non-contaminated sites of affected East European countries. Methods used to assess the caries prevalence were limited to DMFT/dmft (decayed, missing and filled teeth) for the primary and the permanent dentitions. The databases PubMed, EMBASE/Ovid, Cochrane Library, Scopus, and eLIBRARY were consulted for the electronic literature search. Screening of titles and abstracts followed the MOOSE guidelines, while data extraction and the assessment of the full texts were performed in accordance to the Newcastle Ottawa Scale. The statistical analysis revealed considerable heterogeneity of DMFT/dmft values (from I2 = 94% up to I2 = 99.9%; p < 0.05) in children of different ages (5-7; 12-15; and average of 12 years). Scattering of the weighted mean differences (95% CI) ranged from -1.03 (-1.36; -0.7) to 6.51 (6.11; 6.91). Although individual studies demonstrated a greater prevalence of dental caries in children residing in radiation-contaminated areas, no conclusive statement is possible regarding the effect of small dose radiation on the dentition. Hence, further high-quality epidemiologic investigations are needed.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".