Bibliographic record
Abstract
In 2003, 9% of Canadians aged 15 or older reported that they had no natural teeth; that is, they were edentate. This estimate is based on data from the Canadian Community Health Survey (CCHS). A comparison of estimates from the CCHS and the 1990 Health Promotion Survey reveals a decline in the prevalence of complete tooth loss. In 1990, 16% of the population was edentate. The largest decline in edentulism occurred in the older population. Close to half of individuals aged 65 or older (48%) were edentate in 1990; in 2003, the proportion was 30%. This reduction is not surprising given the fairly widespread fluoridation of water and improved access to dental care.1 Other contributing factors include growth of disposable income, improvements in dentist-to-population ratios and expansion of dental insurance coverage.2-4
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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 | 0.000 |
| Open science | 0.000 | 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".