Fenestrated Adipofascial Reverse Flap: A Modified Technique for the Reconstruction of Fingertip Amputations
Bibliographic record
Abstract
AIMS OF THE STUDY: Fingertip injuries can be treated in different ways, including shortening with primary closure, skin graft, and local or distant flaps. Several local flaps for the reconstruction of the amputated fingertip were described. We present our experience with a new concept of homodigital adipofascial reverse flap that avoids the second surgical stage and allows a complete and anatomically perfect reconstruction of nail bed, with preservation of the nail lamina. MATERIALS AND METHODS: Between March 2014 and February 2015, five patients with digital amputations (distally to the nail matrix) were treated using the Fenestrated Adipofascial Reverse (F.A.R.) flap. The patients were evaluated measuring 2-point discrimination (2PD) value and range of motion of the distal interphalangeal joint (DIP). Scar evaluation was performed using the Vancouver Scar Scale (VSS). RESULTS: All the flaps completely survived. A normal nail grow has been observed in first two-three months of post operatory follow-up. Length of the digits was preserved and good aesthetic as functional outcome were archive. The F.A.R. flap provided excellent coverage of fingertip defects and preserved finger length. After 1 year of follow, the mean static 2PD value at the reconstructed finger was 4.2 mm (range 3-5 mm), reconstructed fingers' mean range of motion for the DIP joint was 78 degrees and the VSS score ranged from 0 to 2 (mean score: 0.6). No complications were reported. CONCLUSIONS: F.A.R. flap is one of the most useful techniques in order to achieve all the goals in fingertip 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 | 0.000 | 0.000 |
| 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".