Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of hospital emergency room visits for dental problems not associated with trauma in Canada, and to explore the characteristics that influence such visits. METHODS: Data were collected through a cross-sectional and retrospective national telephone interview survey of 1005 Canadians aged 18 years and over using random digit dialling. Participants were asked if they had ever visited a hospital emergency room for a dental problem not associated with trauma. Descriptive and logistic regression analyses were undertaken. RESULTS: A total of 54 people, or 5.4% of the sample reported having to visit an ER in the past for a dental problem not associated with trauma. Income, painful aching in one's mouth in the previous month, and having to spend a day in bed because of a dental problem in the last 2 weeks, appear to be the dominant predictors of this outcome. CONCLUSIONS: Access to dental insurance or public care mitigates the use of hospital care for dental problems that are best treated in the dental care setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".