Cost-Effectiveness Analysis of Regenerative Endodontics versus MTA Apexification
Bibliographic record
Abstract
INTRODUCTION: With the introduction of stem cell engineering in dentistry, regenerative endodontics has emerged as a potential alternative to mineral trioxide aggregate (MTA) apexification in the management of necrotic immature permanent teeth. However, the utility of this modality in terms of cost-effectiveness has not yet been established. Therefore, we performed cost-effectiveness analysis to determine the dominant treatment modality that would influence decision making from the private payer perspective. METHODS: A Markov model was constructed with a necrotic immature permanent tooth in a 7-y-old patient, followed over the lifetime using TreeAge Pro Healthcare 2022. Transition probabilities were estimated based on the existing literature. Costs were estimated based on United States health care, and cost-effectiveness was determined using Monte Carlo microsimulations. The model was validated internally by sensitivity analyses, and face validation was performed by an experienced endodontist and health economist. RESULTS: In the base-case scenario, regenerative endodontics did not turn out to be a dominant treatment option as it was associated with an additional cost of USD$1,012 and fewer retained tooth-years (15.48 y). Likewise, in the probabilistic sensitivity analysis, regenerative endodontics was again dominated by apexification against different willingness-to-pay values. CONCLUSION: Based on current evidence, regenerative endodontic treatment was not cost-effective compared with apexification in the management of necrotic immature permanent teeth over an individual's lifetime. KNOWLEDGE TRANSFER STATEMENT: The study provides valuable insight regarding the cost valuation and cost-efficacy of regenerative endodontic treatment versus apexification in the management of necrotic immature permanent teeth, as this would aid in effective clinical decision making, allowing for the functional allocation of resources.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".