The relationship between traumatic dental injuries and adolescents' development along the life course
Bibliographic record
Abstract
UNLABELLED: Few models have been proposed to explain the aetiology of traumatic dental injuries. Those that have, focus on risk factors at present stage of life. The contribution of risk factors for dental injuries at different stages of life needs to be investigated. OBJECTIVES: To test the relationship between life course experiences and the occurrence of traumatic dental injuries in adolescents. METHODS: A cross-sectional survey was used to collect data retrospectively. Out of a total number of 764 eligible 13-year-old-adolescents enrolled in private and public schools located in urban areas in the town of Cianorte, Brazil, 652 (85%) agreed to participate in the study. They were interviewed and examined for traumatic dental injuries by two trained dental epidemiologists using validated criteria. The interviews collected information on socioeconomic circumstances, family related variables, school grade and anthropometric measures (height and weight). RESULTS: Adolescent boys, those from non-nuclear families, those reporting high levels of paternal punishment and those who were at lower grades at school for their age were more likely to experience dental injuries than girls, adolescents from nuclear families, those reporting low levels of paternal punishment and those who were at higher grades at school. CONCLUSION: It was concluded that adolescents who experienced adverse psychosocial environments along the life course had more traumatic dental injuries than their counterparts who experienced more favourable environments.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".