Predictors of Multiple Tooth Loss Among Socioculturally Diverse Elderly Subjects
Bibliographic record
Abstract
PURPOSE: This study identifies clinical factors that predict multiple tooth loss in a socioculturally diverse population of older adults. MATERIALS AND METHODS: A total of 193 participants from English-, Chinese-, or Punjabi-speaking communities in Vancouver, British Columbia, with low incomes and irregular use of dental services were followed for 5 years as part of a clinical trial of a 0.12% chlorhexidine mouthrinse. The participants were interviewed and examined clinically, including panoramic radiographs, at baseline and annually for 5 years. Binary logistic regression was used to test the hypothesis that there was no difference between incidence of multiple (≥ 3) tooth loss in older people with various biologic, behavioral, prosthodontic, and cultural variables over 5 years. RESULTS: Multiple tooth loss, which was distributed similarly among the groups in the trial, occurred in 39 (20%) participants over 5 years. The use of removable prostheses was the best predictor of loss, followed by the number of carious surfaces and number of sites with gingival attachment loss > 6 mm. The pattern of prediction was consistent across the three linguocultural groups. CONCLUSION: The use of removable dentures was the dominant predictor of multiple tooth loss in the three communities, but that tooth loss was not significantly associated with the cultural heritage of the participants.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".