Etiology and environment of dental injuries in 12‐ to 14‐year‐old Ontario schoolchildren
Bibliographic record
Abstract
This study reports on the etiology and environment where dental injuries occurred and assesses the relationship between dental trauma, socio-economic status and dental caries experience. A population-based, matched case-comparison study was undertaken in 30 schools in two Ontario communities. Dental hygienists calibrated in the use of the Dental Trauma Index (DTI) screened 2422 children aged 12 and 14 years using DTI and Decayed, Missing and Filled Teeth indices. Cases (n = 135) were children with evidence of dental injury. Controls (n = 135) were children randomly selected after screening and matched with cases according to age and gender. Questionnaires were mailed to parents and children. Prevalence of dental injury was 11.4%, mostly minor injuries 63.7% (enamel fracture not involving dentin), affecting one upper central incisor (70.4%). The mean age at the time of dental injuries was 9.5 years (SD = 1.49; range: 6-13 years). Dental trauma most often occurred among boys at school because of falls or while playing sports. The relationship between dental injuries and the socio-economic indicators chosen was not statistically significant. However, a statistically significant direct relationship (P < 0.001) was shown between increased caries experience and dental injuries. This should focus attention on possible common-risk factors such as health-related behavioral problems that may affect both dental disease and dental injuries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".