Cost‐effectiveness modeling of dental implant vs. bridge
Bibliographic record
Abstract
AIMS: We assess the cost-effectiveness of dental implant first-line strategy vs. fixed partial denture strategy in patients suffering from one single missing tooth. MATERIALS AND METHODS: The model used a simulation decision framework over a 20-year period. Potential treatment switches can occur every 5 years. Transition probabilities come from literature, epidemiological reports or expert opinions. They have been programmed using specific distribution ranges to simulate the patients' and practice variability, and to take into account parameter uncertainty. Direct medical costs have been assessed according to a cost survey. Probabilistic sensitivity analyses were conducted using 5000 Monte-Carlo simulations, generating confidence intervals of model outcomes. RESULTS: We found that mean cost-effectiveness of the bridge strategy is higher than the implant strategy. CONCLUSION: Implant as the first-line strategy appears to be the 'dominant' strategy, considering the lower overall costs and the higher success rate.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".