Flap reconstruction of post-burn neck contractures: A systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Post-burn neck contractures present a complex reconstructive challenge. Flap-based reconstruction remains the standard in surgical management, yet no consensus exists regarding optimal flap selection or outcome evaluation. This systematic review aims to evaluate the effectiveness of flap reconstruction for post-burn neck contractures and to identify gaps in the literature. METHODS: A systematic search of MEDLINE, Embase, and Web of Science was conducted following PRISMA guidelines. Studies reporting flap-based reconstruction for post-burn neck contractures published from inception to January 2025 were included. RESULTS: 54 studies encompassing 1346 subjects and 1466 flap procedures were identified. Scapular/parascapular (27.3 %) and supraclavicular artery (25.7 %) flaps were the most frequently reported among a diverse range of techniques. Functional outcomes, mainly assessed by range of motion, were reported in 83.3 % of studies, with over 90.0 % of patients achieving near-normal mobility. Aesthetic outcomes, mostly based on subjective observations, were reported in 90.7 % of studies, with the majority demonstrating favorable improvements. Patient satisfaction was reported in 33.3 % of studies and was consistently high. Major complications (5.7 %), minor complications (7.2 %), and contracture recurrence (<1.0 %) were rare. There were no significant differences in complication rates between local, pedicle and free flaps (p > 0.05). DISCUSSION: Flap-based reconstruction reliably achieves positive outcomes following post-burn neck contractures. However, inconsistent use of contracture classification systems and lack of standardized, objective, and patient-reported outcome measures limit cross-study comparability. While treatment algorithms exist, they remain underutilized in practice. SUMMARY AND CONCLUSION: Surgeons can confidently select from a variety of flap options to effectively reconstruct post-burn neck contractures. Nonetheless, significant heterogeneity in outcome reporting limits direct comparisons across studies. To address this, we propose a standardized guide for consistent and comprehensive outcome reporting to improve comparability, inform clinical decision-making, and ultimately enhance patient care.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".