Are people who still have their natural teeth willing to pay for mandibular two‐implant overdentures?
Bibliographic record
Abstract
AIM: Oral health in Canada and most developed and developing countries is funded by private payers, whose acceptance of treatment depends on their valuation of it. This study aims to determine how dentate individuals in Quebec, Canada, would value the benefits of mandibular two-implant overdentures based on their willingness to pay (WTP) for the treatment, either directly or with insurance/government coverage. METHODS: A total of 39 individuals (23-54 years) completed a Web-based WTP survey that consisted of three cost scenarios: (a) out-of-pocket payment; (b) private dental insurance coverage; and (c) public funding through additional taxes. Variations in WTP amounts were measured using regression models. RESULTS: Among respondents who were dentate or missing some teeth, average WTP out of pocket for implant overdentures was CAD$5419 for a 90% success rate. They were willing to pay an average CAD$169 as one-time payment for private dental insurance, with a one in five chance of becoming edentate. WTP amounts increased substantially with the probability of success of implant overdenture therapy. The results of regression analyses were consistent with theoretical predictions for education level and income (P < 0.05). CONCLUSIONS: The results of this study, within its limitations, suggest that dentate individuals would be willing to pay a significant amount to receive mandibular two-implant overdentures if and when they become edentate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".