Associations between early childhood caries and malnutrition and anemia: A global perspective
Bibliographic record
Abstract
Abstract Background To determine the relationship between country-level prevalence of early childhood caries (ECC), malnutrition and anemia in infants and preschool children.Methods Matched country level ECC, malnutrition and anemia prevalence information were generated from databases covering the period from 2000 to 2017. Multivariate general linear models were developed to assess the relationship between outcome variables (prevalence of stunting, wasting, overweight, and anemia) and the explanatory variable (ECC prevalence) adjusted for Gross National Income per capita. The adjusted regression coefficients (B) and partial eta squared were computed.Results The mean (standard deviation (SD)) ECC prevalence for 0-2 year-olds was 23.8 (14.8)% and 57.3 (22.4)% for 3-5 year-olds. The mean (SD) prevalence of wasting was 6.3 (4.8)%, overweight was 7.2 (4.9)%, stunting was 24.3 (13.5)%, and anemia was 37.8 (18.1)%. For 0-2-year-olds, the strongest and only significant association observed was between the prevalence of ECC and overweight (η2= 0.21): one percent higher ECC prevalence was associated with 0.12% higher prevalence of overweight (B= 0.12, P= 0.03). In 3-5-year-olds, the strongest and only significant association was observed between the prevalence of ECC and anemia (η2= 0.08): one percent higher prevalence of ECC was associated with 0.14% lower prevalence of anemia (B= -0.14, P= 0.048).Conclusion There were age-related disparities in the relationship between country-level prevalence of ECC, malnutrition and anemia. The relationship between ECC and overweight may be due to intake of sugars. The relationship between ECC and anemia needs further investigations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".