Local Flaps, Including Pedicled Perforator Flaps
Bibliographic record
Abstract
LEARNING OBJECTIVES: After reading this article, the participant should be able to: 1. Discuss the types of local flaps. 2. Analyze the advantages, disadvantages, and applications for each kind of flap. 3. Perform appropriate design and dissection techniques of local flaps. 4. Describe appropriate design and dissection techniques of local perforator and propeller flaps. SUMMARY: The purpose of this article is to comprehensively review the topic of local flaps. Local flaps are those that are elevated nearby and then transferred to an adjacent wound. Options include geometric local flaps, axial pattern local flaps and a new exciting group of flaps, local perforator flaps. The principles, advantages, disadvantages, and applications for each are carefully analyzed. Local perforator flaps can be harvested virtually anywhere in the body and represent a significant clinical advance, as these can solve a wide variety of clinical challenges. These flaps do require gentle microsurgical dissection technique with careful handling for inset of the flap and simultaneously provide the same advantages of other types of local flaps because they also use nearby tissues with a similar color match, thickness, and texture, with primary donor-site closure possible. Local perforator flaps are another very useful option that undoubtedly will become more popular as more surgeons become more familiar with their use and advantages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".