Hand transplantation: current challenges and future prospects
Bibliographic record
Abstract
Abstract: Over the last two decades, 113 vascularized composite allotransplantation (VCA) of the hand have been performed in 76 patients globally. The procedure that was once regarded as experimental has certainly emerged as a clinical reality with multiple centers worldwide now performing it. The psychological and physical impact of losing an upper extremity is profound. Amputees face significant challenges contributing to disability and dependence even with activities of daily living. Hand transplantation offers functions with restoring sensation, voluntary motor control, and proprioception, as well as a sense of feeling “whole” again. Along with these benefits, however, transplantation carries a significant risk profile attributed to the complications of life-long immunosuppression and possible rejection. Moreover, the procedure carries a significant financial burden to the health care system. As hand VCA is becoming more widely accepted and performed worldwide, there are still many challenges that will face its rapid growth. This review highlights some of the challenges facing hand VCA including patient selection, effect on quality of life, financial burden, functional outcomes, and complications of immunosuppression. Keywords: hand transplantation, vascularized composite allotransplantation A Letter to the Editor has been received and published for this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".