Predicted Cost Analysis for Solid Organ Transplantation in Private Practice in Greece
Bibliographic record
Abstract
Introduction: Involvement of the private health sector in the field of solid organ transplantation was enabled by law for the first time in Greece in 2011. An analysis of the predicted cost of initiating a transplant program, especially considering the country's current austerity, is of vital importance before launching such an attempt. Methods: A detailed material and transportation cost analysis was performed. The budget units under consideration were pre-transplant patient evaluation, operative procedure (organ procurement & transplantation), and uneventful postoperative course for kidney (6 days) and liver (15 days) transplantation. Surgeons' fees and calculated profit for the providing private healthcare company were not included in the study. Results:Conclusion: To the best of our knowledge, this is the first cost analysis of solid organ transplantation procedures in private practice in Greece. Given the recent failure of introducing diagnosis-related groups (DRGs) within the framework of the country's public health sector, this study currently depicts the only opportunity for ascertainment of expenses in the field of transplantation. Furthermore, it strengthens the argument for Greek patients avoiding expensive treatment abroad and even more, it opens the perspective of foreign patients undergoing cost-effective treatment in Greece.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".