Effects of nutrition on timing of mineralization in teeth in a Peruvian sample by the Cameriere and Demirjian methods
Bibliographic record
Abstract
BACKGROUND: Few studies have been conducted among children to investigate the effects of malnutrition and race on the timing of tooth formation. AIM: The study investigated whether there is a significant association between nutritional status, gender, and the process of tooth mineralization. SUBJECTS AND METHODS: Orthopantomograms of 287 Peruvian schoolchildren, aged 9.5-16.5 years, were evaluated. For each individual, we considered the number of the seven right permanent mandibular teeth, with completely closed apical ends of roots (N0), sum of normalized open apices (S), and the Demirjian score (Ds). We also estimated individual age by the Cameriere and Demirjian methods, and assessed their accuracy. RESULTS: For each age class, the distributions of N0, S and Ds in the two sub-populations of Peruvian children, undernourished and well nourished, were not statistically significant. The mean error (ME) in age estimation was 0.75 and 1.31 years for the Cameriere and Demirjian methods, respectively. CONCLUSIONS: Nutrition did not seem to affect the process of tooth growth. As regards the accuracy of age estimation, the Cameriere method yielded more accurate estimates than the Demirjian method.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".