Postoperative Outcomes of Local Skin Flaps Used in Oncologic Reconstructive Surgery of the Upper Cutaneous Lip: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Despite many options for upper lip reconstruction, each method's advantages and disadvantages are unclear. OBJECTIVE: To summarize complications and functional and aesthetic outcomes of localized skin flaps for oncological reconstruction of the upper cutaneous lip (PROSPERO CRD42020157244). METHODS: The search was conducted in Ovid MEDLINE, Ovid EMBASE, and CENTRAL on December 14, 2019. Two reviewers screened 2,958 results for eligibility. Bias assessment was conducted using ROBINS-I criteria. RESULTS: Our search identified 12 studies reporting outcomes of V-Y advancement, ergotrid, rotation, Karapandzic, alar crescent, and propeller facial artery perforator flaps. Flap complications (infection, hemorrhage/hematoma, wound dehiscence, and flap necrosis) ranged from 0% to 7.69%. Functional outcomes (salivary continence, microstomia, and paresthesia) were poorest for Karapandzic flaps. Aesthetic outcomes, when reported, stated satisfaction rates greater than 90%. V-Y advancement flaps reported the highest rates of poor scarring (0%-20%) and need for revision surgery (0%-46.7%). CONCLUSION: Our results provide dermatologic surgeons an overview of upper cutaneous lip flap outcomes reported in the literature. In general, we noted high patient satisfaction rates and low complication rates. Additional research into outcomes of other commonly used flaps is needed. Standardization of reported outcomes could allow further comparison across different flaps or across studies of the same flap.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".