The effect of dental insurance on the ranking of dental treatment needs in older residents of Durham Region's homes for the aged.
Bibliographic record
Abstract
The effect of dental insurance on the ranking of dental needs in older adults has not been reported previously. We examined this effect using data obtained from a cross-sectional survey of older adults living in homes for the aged in Durham Region, Ontario. History of dental insurance was obtained during interviews. Dental needs, assessed during clinical examinations, were ranked from no need to urgent need according to the guideline of the American Dental Association. The associations between the rank of dental needs, dental insurance and other factors were analyzed with the Kruskal Wallis test, chi-square test, analysis of variance and multiple logistic regression. Of the 252 participants, 80 (31.7%) had been insured continuously since 1974, 69 (27.4%) had no need for dental treatment and 59 (23.4%) needed urgent dental care. More of the continuously insured than the uninsured residents were dentate (46/80 [57.5%] vs. 75/172 [43.6%], p = 0.04). Ranking of the need for care was not significantly influenced by dental insurance; need of any kind was explained by being dentate (odds ratio 12.3, 95% confidence interval 5.6 27.3).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".