Functional outcomes after tendon transfers in upper extremity reconstruction: A comprehensive systematic review
Bibliographic record
Abstract
This systematic review examines the effect of functional outcome on tendon transfer in upper extremity reconstruction, considering motor aspects, strength, range of motion (ROM), patient-reported outcomes (PRO), and psychosocial outcomes. We used PubMed, Embase, Cochrane Library, Scopus, Web of Science, and CINAHL databases and, consistent with PRISMA guidelines, searched these databases until July 2025 with randomized controlled trials, cohort studies, and case series (>=10 patients) in all ages and causes (traumatic, neurological, congenital, degenerative). Exclusion criteria included case reports, studies of <10 patients, or non-upper extremity transfers. Studies were screened, and two reviewers extracted data on study characteristics, patient demographics, surgical techniques, and outcomes (MRC scale, dynamometry, goniometry, DASH, SF-36, and return-to-work status). Bias was assessed using the Cochrane and Newcastle-Ottawa tools. Subgroup analyses were by region of anatomical involvement (shoulder, elbow, wrist, hand), etiology, age, and socioeconomic situation (if a sufficient number of studies permitted it). Comparisons were made to alternatives (i.e., nerve transfers). Among the main findings, differences in functional recovery can be observed in patient factors (such as age and compliance), surgical practice, and access to rehabilitation. Severe outcomes (tendon rupture, etc.) were studied. In the review, research limitations are identified, highlighting gaps that provide evidence-based guidance to clinicians and inform future strategies for advancing upper extremity reconstruction.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".