Debulking and Osteotomy Procedures to Correct Severe Macrodactyly Deformity of the Hand in Young Patients
Bibliographic record
Abstract
BACKGROUND: This study evaluated early debulking combined with corrective osteotomy for the treatment of hand macrodactyly in young patients. METHODS: This retrospective study included 31 young patients (mean age, 12.7 years) with severe hand macrodactyly. All the patients underwent microsurgical debulking based on multiple pedicled flaps and osteotomy to reduce soft-tissue volume and correct finger deformity. The mean follow-up period was 3.1 years. Clinical evaluations included ratios of finger length, circumference, and nail dimensions compared with the intact fingers; metacarpophalangeal joint range of motion; and functional scores (Kapandji, Action Research Arm Test, and Barthel) before and after surgery. The Vancouver Scar Scale score, parent satisfaction, and complications were also assessed. RESULTS: The procedure preserved all the fingers, avoided ablation, and restored near-normal appearance. Preoperatively, affected fingers were 1.3 times longer, with proximal interphalangeal joint and distal interphalangeal joint circumferences 1.5 and 1.7 times larger than those of intact fingers. At the last follow-up, these ratios improved to 1.1, 1.2, and 1.2, respectively. Nail length and width, initially 1.7 and 1.6 times larger, normalized to 1.0 and 1.1 times larger. Metacarpophalangeal joint range of motion improved from 41 degrees to 69 degrees. Functional scores rose significantly: Kapandji from 6 to 9, Action Research Arm Test from 33 to 53, and Barthel from 95 to 98. The average Vancouver Scar Scale score was 2. All parents expressed satisfaction with the results. CONCLUSION: Early debulking combined with corrective osteotomy reduces finger volume, preserves digits, improves functional outcomes, and minimizes adverse effects on quality of life, offering a promising option for treatment of severe hand macrodactyly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".