[Application of combined anterolateral thigh-ilioinguinal Flow-through flaps in repairing complex lower limb defects].
Bibliographic record
Abstract
Objective: To evaluate the effectiveness of combined anterolateral thigh-ilioinguinal Flow-through flaps for repairing complex lower limb defects. Methods: =3). The size of soft tissue defects ranged from 23 cm×8 cm to 44 cm×12 cm. Reconstruction employed combined anterolateral thigh-ilioinguinal Flow-through flaps in the size of 24 cm×10 cm to 48 cm×14 cm. The recipient sites were sutured in primary closure in 12 cases, and 8 cases had no available vascular anastomosis sites in the recipient sites, and a cross-leg flap form was used to establish a temporary blood supply, and the flaps were cut off after 3-4 weeks. The donor sites in the thigh were directly sutured. During follow-up, the survival of the flaps, appearance, texture, and related complications were observed; the Vancouver scar scale (VSS) score was used to evaluate the scar condition of the flaps, the lower extremity function scale (LEFS) score was used to evaluate the function of the affected lower limb, and the visual analogue scale (VAS) score was used to evaluate the pain condition of the affected side. Results: <0.05). Conclusion: The anterolateral thigh-ilioinguinal Flow-through flaps for repairing complex lower limb injuries is a good method. The distal blood supply of the affected side recover well, the survival rate of the flap is high, and the function recovery of the affected limb is good.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".