Tendon Function After Replantation: Prognostic Factors and Strategies to Enhance Total Active Motion
Bibliographic record
Abstract
In Brief The purpose of this study was to determine the important prognostic factors for recovery of tendon function as measured by total active movement (TAM) in patients undergoing digital replantation. More important, the authors wanted to establish which factors may be manipulated to maximize motion. A retrospective review of 48 patients (103 digital rays) who underwent replantation was performed. Average TAM for all digits was 129 deg. Zone 1 and zone 5 injuries had better TAM than injuries in zones 2, 3, and 4, which had TAM values not significantly different from one another. Avulsion injuries fared significantly worse than other mechanisms of injury. TAM values were not affected by age, type of bone fixation, number of arteries repaired, or number of digits injured. Digits with both the profundus and the superficialis tendons repaired had significantly better TAM values relative to one-tendon fingers. Similarly, fingers treated with an “early” mobilization regime also exhibited better movement. Small numbers of injured digits in some groups may have limited our ability to detect significant differences. A retrospective review of 103 digital replantations in 48 patients disclosed superior tendon function in Zone 1 and 5 injuries, non-avulsive injuries, two-tendon repairs, and “early mobilization” regimens. Results were unaffected by age, type of bone fixation, number of arteries repaired, or number of digits injured.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".