26. “The cost of facing life”: Systematic Review and AI assisted econometric analysis of the Global Price Tag of Facial Transplantation
Bibliographic record
Abstract
Introduction: Since the first facial transplant in 2005, more than 50 successful procedures have been performed in 10 countries. The costs and the diversity of economic profiles of the countries performing this procedure are a question mark. Differences in the economic potentials of each country involved may be the key to cost variability, which are measured by specific econometric measures (EM), such as gross domestic product (GDP), current health expenditure (CHE) and health expenditure per capita (CHPC). This work has the general objective to determine, compare and analyze the global cost of face transplantation Methodology: Longitudinal descriptive retrospective study analyzing the costs of facial transplantation worldwide. A systematic review of articles from 2005 onward was conducted using PUBMED, GOOGLE SCHOLAR, and COCHRANE, with keywords like “face transplant,” “facial transplantation,” “cost,” and “analysis.” Inclusion criteria required data on the country of origin, procedure cost, and publication details, excluding duplicates or articles without detailed cost analysis. Using artificial intelligence (AI) (OpenEvidence App), the most common procedures for complex facial reconstruction were identified and their costs analyzed globally. Values were converted to USD and averaged, then compared with GDP and CHPC using data from the World Bank. The cost analysis used a formula comparing the procedures costs to EM. Results: Out of 26,955 articles, a total of 3,178 articles were found adding “analysis” and only 98 by using the word “cost”. Ending with 7 articles and costs reported from: United States, Canada, France, China and Finland. With an average cost of $293,083 and the highest price being in Finland with $406,559 and the lowest China with $80,000 AI identified the most used procedure for complex facial reconstruction as free and axial flaps (FF), with a reported cost of $56,205 for the United States, $35,708.50 for Europe and $9,265 for China. CHPC data showed significant variance; (United States) $12,473.79 (Highest). (Canada) $6207.18. (Spain), $3,234.29 (Finland) $5,488.00. (China), $670.51. (France), $5,380.88. FT The ratios of CHPC to FF and CHPC to FT are shown in the attached image. The highest ratio for FF was China (13.81) and the lowest was the United States (4.5); for FT, the highest reported ratio was China (119.31) and the lowest was the United States (25.09). Conclusion: The real value of face transplantation goes beyond the economic, when analyzing the CHPC/FF and CHPC/FT, it suggests that the highest monetary value recorded is not the most expensive, since it is determined by the economic effort of each country. In conclusion, face transplantation does not require great economic potential, but a great willingness to invest in it.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".