Management of Digital Tendon Avulsion at the Musculotendinous Junction of the Forearm: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Tendon avulsion at the musculotendinous junction caused by digit avulsion amputation or closed injury is a challenging problem, for which the literature lacks definitive recommendations regarding treatment. We have provided a systematic review and developed an algorithm to delineate optimal management of this injury. METHODS: Two independent reviewers undertook a systematic review of the literature to identify articles discussing management of forearm tendons avulsed at their musculotendinous junction. Patient demographics, injury mechanism, injury pattern, type of repair, and outcome were investigated. These data were analyzed to reveal tendencies in management, which were then organized into an algorithm. RESULTS: Twenty articles fit our criteria for a total of 91 tendons. Cases were mostly males involved in work accidents. Treatment options were tendon resection, reattachment to muscle, tendon transfer, and side-to-side repair. When the digit was replanted, tendons avulsed through avulsion amputations were preferentially treated by reattachment in the case of the thumb, transfers for the index and long fingers, and resection for the ring and small fingers. Reattachment was favored for metacarpophalangeal level amputations, while transfer was selected for proximal phalanx levels. For closed avulsion injuries, flexors were preferentially treated with reattachment or transfer, while extensors underwent transfer or side-to-side repair. CONCLUSIONS: In the management of tendon avulsions at the musculotendinous junction, specific procedures are favored depending on the mechanism of injury, the type of tendon and digit involved, and the level of bone amputation. An algorithm is presented to facilitate optimal treatment based on these injury characteristics.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".