Repairment of head soft-tissue defect after electrical injury with free ALTP flap
Bibliographic record
Abstract
Abstract Objective: To investigate the feasibility and effectiveness of free ALTP flap in the repairment of the large head soft-tissue defect after electric shock injury. Methods: In this study, 25 patients underwent the reconstruction to repair the defect of the head by ALTP, including 18 males and 7 females (mean age:43 years; ranged 20 to 53 years). The etiologies of all patients were electric shock. The location of the defects involved 11 occipitalia,6 tempus,3 forehead and 5 crown, while the dimension of the defects ranged 7*5 to 21*10 cm2(mean 18.5*9.5 cm2). Results: 25 patients were successfully treated by ALTP. Reconstructions were performed at the second stage in all cases, and all flaps survived completely. The donor sites were closed primarily in nearly all cases with an exception of 5 cases, which were covered with skin-grafting. The Pain Score was from 0 to 45 points with an average of 20 points. The score of Vancouver Scar Scale of donor sites was from 2 to 5 points with an average of 3.4 points. All flaps were followed-up for an average of 16 months (ranged from 6 to 24 months). No significant effect on quadriceps muscle strength and knee movement. Most cases showed satisfactory outcomes. Conclusion: The use of free ALTP for the repairment of large head defects after electroshock injury is worthy of clinical promotion and application because of its strong resistance to infection, high repair fit and rapid postoperative recovery.
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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) | 0.000 | 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".