Functional Outcomes of Major Upper Extremity Replantation: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Traumatic upper limb amputations proximal to the carpus are devastating injuries. Existing literature on outcomes following replantation is limited. Our objective was to perform a scoping review of (1) functional outcomes; (2) return to work data; and (3) secondary surgeries required following proximal to carpus replantation. METHODS: A literature search of MEDLINE, EMBASE, Cochrane, and CINAHL was performed according to PRISMA guidelines. All studies reporting on functional outcomes, return to work, or secondary surgeries following replant of traumatic proximal to carpus amputation were included. RESULTS: Of the 753 articles, 13 studies were included, accounting for 136 major upper extremity replants (0 shoulder, 36 arm, 14 elbow, 86 forearm). Average age was 35 (24 -47) years, with average follow-up of 8 years (2 -18). Chen's Functional Criteria was the most common tool for reporting outcomes (10/13). Level of injury was related to functional outcome, with excellent to good Chen scores for replants distal to elbow, and poor Chen scores for replants at or proximal to elbow. Return to work correlated with level of replantation, with successful return for 65% of forearm, 43% of elbow, and 32% of arm replants. A mean of 2.4 secondary procedures were required. CONCLUSIONS: This study provides insight into major upper extremity replantation, to assist in patient counseling and surgical decision making. Good functional outcomes and successful return to work are directly related to level of injury following major upper extremity replant. Patients should be counseled that more than 1 secondary procedure may be required.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".