Comparative Retrospective Analysis of Cross-Finger Flap Outcomes: A Study on Split-Thickness Skin Graft vs. Full-Thickness Skin Graft in Donor Fingers
Bibliographic record
Abstract
INTRODUCTION: Hand injuries, particularly those involving the fingers, are complex and often necessitate meticulous surgical interventions. Cross-finger flaps (CFFs) are a reliable technique for covering finger defects, with the choice of skin graft at the donor site playing a crucial role in the procedure's success. Split-thickness skin grafts (STSGs) and full-thickness skin grafts (FTSGs) are commonly used, each offering distinct advantages and drawbacks. This study compares the functional and aesthetic outcomes of donor fingers covered with STSG versus FTSG in CFF procedures. MATERIALS AND METHODS: This retrospective observational study was conducted from January 2020 to 2024. A total of 82 patients who underwent CFF surgery were included, with 41 patients each in Group A (STSG) and Group B (FTSG). The functional and aesthetic outcomes were assessed. Statistical analysis was performed using SPSS version 26.0 (IBM Corp., Armonk, NY), with significance set at p < 0.05. RESULTS: The study revealed that Group B had superior outcomes across all measured parameters. The mean visual analog scale (VAS) score for aesthetic outcomes was significantly higher in Group B (8.5 ± 1.2) compared to Group A (7.0 ± 1.5, p < 0.01). Functional recovery in proximal interphalangeal (PIP) and distal interphalangeal (DIP) joints, measured by range of motion, was also better in Group B compared to Group A (p = 0.002). Sensory recovery was more favorable in Group B, with 85% achieving S3+ or better, compared to 60% in Group A (p < 0.05). Additionally, the graft donor site scar was significantly less noticeable in Group B, with a Vancouver Scar Scale score of 3.5 ± 1.1, compared to 5.0 ± 1.4 in Group A (p < 0.01). CONCLUSION: FTSG offers superior functional and aesthetic outcomes compared to STSG in this study. The findings suggest that FTSG should be preferred for covering donor fingers. These results provide strong evidence for the use of FTSG in optimizing surgical outcomes and improving patient satisfaction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".