A cost-effectiveness analysis of an endocrown versus complete crown
Bibliographic record
Abstract
The purpose of this simulation study was to assess the cost-effectiveness of an endocrown versus a complete crown as a definitive restoration for structurally compromised endodontically treated teeth. A Markov simulation model was constructed with endodontically treated permanent molar teeth using TreeAge Pro Healthcare (2023) as a starting point for an 18-year-old patient. Costs were extrapolated from the ADA dental survey based on the United States healthcare, and the probabilities of transition were derived from existing literature. The cost-effectiveness was determined by using Monte Carlo microsimulations. A sensitivity analysis was performed to validate the model internally, whereas an experienced health expert and an endodontist performed the face validation. The complete crown was associated with additional health benefits (1.36 and 0.9 more years over a period of 5 years and lifetime, respectively) but at an increased cost (an additional 1143 USD and 1535 USD over a period of 5 years and lifetime, respectively). Moreover, the endocrown was cost-effective at lower Willingness-To-Pay (WTP) values (92% acceptable at 250 USD for 5 years and 73% acceptable at 250 USD for the lifetime of an individual), whereas at increased WTP threshold values, the complete crown was a cost-effective treatment option (98.6% acceptable at 1250 USD for 5 years and 99.5% acceptable at 8000 USD over an individual's lifetime). The endocrown was a cost-effective restorative option at lower WTP values. However, at an increased WTP threshold, the complete crown became a more cost-effective restoration.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".