Regional variation in dental procedures among people with an intellectual disability, Ontario, 1995-2001.
Bibliographic record
Abstract
BACKGROUND: The literature indicates that people with an intellectual disability have a prevalence of dental caries that is either lower than or similar to that of the general population. However, many of their caries go untreated, and extractions are more often used as a means of treatment than in the general population. A substantial percentage (40%) of day admissions to hospital of people with intellectual disabilities in Ontario is related to dental diseases. In this paper, we examine whether rates of in-hospital dental procedures are evenly distributed across Ontario and discuss possible explanations for the findings. MATERIALS AND METHOD: A retrospective analysis was made of routinely collected hospital admission data for people with an intellectual disability. Age- and gender-adjusted rates for dental procedures were calculated using the direct method of adjustment and 1996 census population estimates of Ontario. Three different summary measures for the assessment of regional variation were used. RESULTS: Two areas had dental procedure rates among those with an intellectual disability that were significantly lower than the overall Ontario rate: Hamilton-Wentworth and Quinte-Kingston and Rideau. The 3 district health council areas with the highest rates for dental procedures were Niagara, Essex-Kent and Lambton, and Durham-Haliburton-Kawartha and Pine Ridge; all 3 rates were higher than the overall Ontario rate. CONCLUSIONS: The use of day surgery and in-hospital visits to treat dental diseases in people with an intellectual disability varies considerably by region in Ontario. Observed differences may indicate inequities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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".