Classification and comparison of impacted tooth extraction costs with OECD countries: Running title : Cost of impacted tooth extraction in OECD countries
Bibliographic record
Abstract
Purpose: The purpose of this study is to compare the cost of impacted tooth extraction in Korea and some OECD countries and ultimately use it as basic data for the future revision of the relative value of national health insurance.Materials and Methods: Costs of tooth extraction in Korea, Japan, the United States, Australia, the United Kingdom, Germany, France, and Canada were investigated. The costs were investigated through the dental association or association of oral and maxillofacial surgery in each country with literature review. In countries such as Korea, Japan, and Germany, which have a universal health insurance system at the national level, a survey was conducted based on a data collection listing standard tooth extraction cost. The costs were compared using the price level of each country and Big Mac Index.Results: The classification and cost system for impacted tooth extraction were different in each country. Nevertheless, when comparing them by grouping them as similar as possible, the cost of impacted tooth extraction of Korea is lowest compared to some OECD countries. In addition, the cost is lowest even considering comparative price levels and the Big Mac index.Conclusion: In conclusion, we believe that the fees for impacted tooth extraction need to be appropriately adjusted to a level similar to that of OECD countries, reflecting various factors. It is also believed that changes and approvals for a new the tooth extraction cost system are needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".