A 10‐year retrospective study of paediatric emergency department visits for dental conditions in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Trends of paediatric emergency visits (ED) for dental conditions have been broadly discussed; however, little has been published in the Canadian context. AIM: To describe the utilization of ED for dental conditions among children and to investigate demographic characteristics influencing its use. DESIGN: A comprehensive review of records of all children aged 1-17 years who attended the ED service of a paediatric hospital in Montreal, Canada, for dental conditions over a 10-year period (2004-2013) was completed. Information on the child's principal dental diagnosis, sociodemographic data, and source of referral was obtained. RESULTS: A total of 10 905 paediatric ED visits were seen during the study period. Among the children, 54.7% were male and the majority was younger than 6 years old. Dental caries constituted the most common reason for ED presentation comprising close to 43% of total visits for a dental complaint. Females, teenagers, and self-referred children were more likely to experience ED visits due to non-traumatic dental conditions. CONCLUSIONS: The utilization of ED for dental conditions has increased among pre-school children in the last decade and was mostly due to caries-related dental problems. Effective preventive strategies are needed to improve the oral health condition of children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".