Reverse Distal Transverse Palmar Arch in Distal Digital Replantation
Bibliographic record
Abstract
BACKGROUND: Refinements in microsurgery have made distal finger replantation an established technique with high success rates and good functional and aesthetic outcomes. However, it still represents a technically demanding procedure due to the small vessel caliber and frequent lack of vessel length, requiring the use of interpositional venous grafts in some instances. We describe a new technique for anastomosis in fingertip replantation, whereby the need for venous grafts is eliminated. METHODS: Applying the reverse distal transverse palmar arch technique, 11 cases of distal digital replantation were performed between January 2011 and July 2016. The described procedure was used for arterial anastomosis in 10 cases and arteriovenous shunting for venous drainage in 1 case. A retrospective case review was conducted. The technical description and clinical outcome evaluations are presented. RESULTS: Ten of the 11 replanted digits survived, corresponding to an overall success rate of 91%. One replant failed due to venous insufficiency. Blood transfusions were not required for any of the patients. Follow-up (range, 1.5-5 months) revealed near-normal range of motion and good aesthetic results. All of the replanted digits developed protective sensation. The average length of hospital admission was 5 days. All patients were satisfied with the results and were able to return to their previous work. CONCLUSIONS: The use of the reverse distal transverse palmar arch is a novel and reliable technique in distal digital replantation when an increase in vessel length is required, allowing for a tension-free arterial repair without the need for vein grafts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".