Classificação das perdas dentárias: fatores associados a uma nova medida em uma população de adultos
Bibliographic record
Abstract
This study evaluated tooth loss and factors associated with a new classification, which considers not only the number of teeth lost, but also the number and position that they occupied in the mouth. In Piracicaba, State of São Paulo, Brazil, 248 adults (20-64 year-olds) were examined using a household probability sample. The oral examinations followed the WHO criteria for caries and periodontal disease. Socioeconomic, demographic and dental service use data were collected. The tooth loss outcome, based on tooth position and number of missing teeth, was analyzed by hierarchical multinomial logistic regression using a conceptual model. The mean number of missing teeth was 8.52 (DP = 9.24). For those who had lost up to 12 posterior teeth, age (PR = 1.1) and low social class (PR = 2.6) were significant; for those who lost up to 12 including anterior teeth, age (PR = 1.1) and clinical attachment loss>4mm (PR = 2.9); and for tooth loss in excess of 13 teeth, age (PR = 1.3), low social class (PR = 3.8), and visiting a dentist due to emergency (PR = 9.4) were significant. Age was associated with tooth loss. The classification made it possible to differentiate variables in accordance with position or the number of teeth lost.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".