Free Tissue Transfers and Replantation
Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Describe the indications and contraindications for free flap reconstruction. 2. Describe the indications, anatomy, harvest technique, and advantages and disadvantages of the workhorse free flaps. 3. Describe the indications and contraindications for extremity replantation. 4. Describe the techniques and management for extremity replantation. SUMMARY: Microsurgical free flap reconstruction uses a multitude of surgical flaps available to meet the needs of the recipient site. These include cutaneous, muscle, bone, fascia, or some combination of these as available options. Furthermore, sophisticated reconstruction has been enhanced by the development of perforator flaps, enabling multicomponent reconstruction to be performed with reduced donor-site morbidity. It is mandatory that proper débridement of the defect be performed before reconstruction, and that the anastomosis is performed without tension or twisting outside of the zone of injury. There are indications for both musculocutaneous and perforator flaps, and selection is dependent on recipient-site characteristics in addition to function and aesthetics of both the recipient and donor sites. Muscle flaps provide well-vascularized pliable tissue and are used for deep space obliteration, whereas fasciocutaneous flaps are used for flatter, more superficial wounds. Microsurgical replantation of an amputated extremity offers a result that is usually superior to any other type of reconstruction. However, replantation of extremities involves more than microsurgery, as repair of bony and tendon injury must be undertaken as well. This article focuses on the indications, technique, and results of free flap reconstruction and replantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.010 |
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".