The Complications of Osseous Reconstruction in the Head and Neck: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the postoperative complications of the fibular free flap (FFF), scapula free flap (SFF), and osteocutaneous radial forearm free flap (OCRFFF) following osseous reconstruction in the head and neck. DATA SOURCES: PUBMED, EMBASE, Cochrane. REVIEW METHODS: A literature search and systematic review were performed using the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. A meta-analysis of proportions was conducted using a random effects model to compare operative time and postoperative complications. RESULTS: The systematic review identified 26 studies comparing at least 1 variable of interest. The odds ratio estimates favored reduced rates of flap failure with the OCRFFF when compared to FFF (0.7, confidence interval [CI]: 0.29-1.11, P < .001), while FFF and SFF were similar. The mean difference estimates for operative time significantly favored FFF over SFF (-51.04 minutes, CI: -92.73 to -9.35, P = .016) and OCRFFF over FFF (66.77 minutes, CI: 52.74-80.8, P < .001). The FFF was more prone to hardware exposure, longer hospital stays, and donor site complications. Recipient wound complications and fistula rates were similar for all flap types. CONCLUSION: Depending on the clinical context, the OCRFFF, FFF, and SFF are all robust options for reconstruction in the head and neck. The OCRFFF is associated with a reduced rate of flap failure and shorter operative times. The SFF requires longer operative times, although significant variation was observed between institutions. The FFF has broad reconstructive indications but is associated with more perioperative and long-term complications.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".