Postoperative Outcomes of Local Skin Flaps Used in Oncologic Reconstructive Surgery of the Nasal Ala: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Despite numerous options for nasal ala reconstruction, advantages and disadvantages of each method are unclear. OBJECTIVE: To summarize reported outcomes of local flaps without the use of grafts for nasal ala oncologic reconstructive surgery. METHODS: A nasal ala-specific protocol was adapted from a previous head- and neck-specific PROSPERO submission (CRD42017071596). The search was conducted in MEDLINE, EMBASE, and CENTRAL on December 23, 2017 and updated on May 10, 2019. Two reviewers screened 9,313 results from head and neck literature. Study bias was evaluated with the ROBINS-I tool. RESULTS: Subunit-based categorization of included studies identified 12 nasal ala-specific publications. Complications (flap necrosis, hematoma, wound infections, trapdoor deformities, and dehiscence), functional (nasal valve or respiratory dysfunction), and cosmetic (alar rim distortion/asymmetry/notching, secondary/revisionary procedures, and patient satisfaction) outcomes were extracted. CONCLUSION: Generally favorable outcomes are seen in all flaps. Careful consideration of donor sites for interpolation flaps is needed for optimal cosmetic outcomes. Transposition flaps, including laterally based bilobed and trilobed flaps, created good outcomes, although melolabial transposition flaps may produce poorer outcomes compared with melolabial island pedicle advancement flaps. Caution is needed for rotation flaps to prevent nasal valve/respiratory dysfunction due to alar crease contracture or ridge elevation. Further research is needed.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".