Composite Tissue Transplant of Hand or Arm: A Health Technology Assessment.
Bibliographic record
Abstract
BACKGROUND: Injuries to arms and legs following severe trauma can result in the loss of large regions of tissue, disrupting healing and function and sometimes leading to amputation of the damaged limb. People experiencing amputations of the hand or arm could potentially benefit from composite tissue transplant, which is being performed in some countries. Currently, there are no composite tissue transplant programs in Canada. METHODS: We conducted a systematic review of the literature, with no restriction on study design, examining the effectiveness and cost-effectiveness of hand and arm transplant. We assessed the overall quality of the clinical evidence with GRADE. We developed a Markov decision analytic model to determine the cost-effectiveness of transplant versus standard care for a healthy adult with a hand amputation. Incremental cost-effectiveness ratios (ICERs) were calculated using a 30-year time horizon. We also estimated the impact on provincial health care costs if these transplants were publicly funded in Ontario. RESULTS: Compared to pre-transplant function, patients' post-transplant function was significantly better. For various reasons, 17% of transplanted limbs were amputated, 6.4% of patients died within the first year after the transplant, and 10.6% of patients experienced chronic rejections. GRADE quality of evidence for all outcomes was very low. In the cost-effectiveness analysis, single-hand transplant was dominated by standard care, with increased costs ($735,647 CAD vs. $61,429) and reduced quality-adjusted life-years (QALYs) (10.96 vs. 11.82). Double-hand transplant also had higher costs compared with standard care ($633,780), but it had an increased effectiveness of 0.17 QALYs, translating to an ICER of $3.8 million per QALY gained. In most sensitivity analyses, ICERs for bilateral hand transplant were greater than $1 million per QALY gained. A hand transplant program would lead to an estimated annual budget impact of $0.9 million to $1.2 million in the next 3 years, 2016 to 2018, to treat 3 adults per year. CONCLUSIONS: Composite tissue transplant of the hand or arm may improve a patient's ability to function, but because the overall quality of evidence is of very low quality, there is considerable uncertainty as to whether benefits outweigh harms. Compared with standard care, both single- and double-hand transplants are not cost-effective.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".