Impact of treated and untreated dental injuries on the quality of life of Ontario school children
Bibliographic record
Abstract
A population-based, matched case-comparison study was undertaken in 30 schools in two Ontario communities to measure the impact of dental trauma on quality of life (QoL) in Canadian school children. Dental hygienists screened 2422 children aged 12-14 years using the dental trauma index, the decayed, missing and filled teeth index (DMFT) and the aesthetic component of the index of orthodontic treatment needs (AC-IOTN). Cases (n = 135) were children with evidence of previous dental trauma. Controls (n = 135) were classmates matched for age and gender. Oral-health-related QoL was assessed using mailed Child Perception Questionnaires (CPQ(11-14)) completed by all children. Data were analyzed using simple and multiple conditional logistic regressions after adjusting for DMFT and AC-IOTN, CPQ(11-14), overall impact and item-specific impacts. Approximately 64% of injuries were untreated enamel fractures and just over 30% were previously injured restored teeth. Untreated children experienced more chewing difficulties (P = 0.026), avoided smiling (P = 0.029) and experienced affected social interactions (P = 0.032) compared with their non-injured peers. When treated and non-injured groups were compared, the only statistically significant effect was difficulty in chewing (P = 0.038). Injured children who were untreated experienced more social impact than their non-injured peers. Restoration of injured teeth improved aesthetics and social interactions but functional deficiencies persisted as a result of periodontal or pulpal pain.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".