Use of Distally Based Sural Artery Flap to Manage the Soft Tissue Defects of Lower Tibia and Ankle
Bibliographic record
Abstract
Objective: To present experience of soft tissue cover of lower one third of tibia and ankle treated by an orthopaedic surgeon without the presence of a plastic surgeon but of course, depending on the reliability of this flap. Patients and Methods: Nineteen patients, fifteen males and four females, with soft tissue defect of lower one third tibia and ankle requiring soft tissue cover were treated from April 2002 to September 2005. The flap was outlined at the posterior aspect of junction of upper and middle 1/3 leg. The pivot point of the pedicle was at least 5cm i.e., 3 fingers’ breadth above the lateral mallelous to allow anastomosis with the peroneal artery. Skin incision was started along the line in which the fascial pedicle would be taken. The sub dermal layer was dissected to expose the sural nerve, accompanying superficial sural vessels and short saphenous vein. The subcutaneous fascial pedicle was elevated, with a width of 2cm to include the nerve and these vessels. At the proximal margin of the flap, the nerve and the vessels were ligated and severed. The skin island was elevated with the deep fascia. The donor site defect was closed directly when the flap was less than 3cm wide. A larger donor site defect along with the pedicle was covered with a split thickness skin graft. Results: All flaps except two survived. Most flaps showed slight venous congestion which cleared in a few days. There was no loss of split skin graft & none was lost to follow up.Conclusion: Distally based Sural artery flap remains the choice for reconstruction of soft tissue defects of lower 1/3 tibia and ankle. The dissection is easy, quicker and can be done by an orthopaedic surgeon already involved in flap surgery; without the presence of plastic surgeon.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".