[Application of hairpin shaped incision combined with cover-lifting flap in plastic surgery of huge fat pad on nape and back].
Bibliographic record
Abstract
Objective: To explore the effectiveness of hairpin shaped incision combined with cover-lifting flap in plastic surgery of huge fat pad on nape and back. Methods: Between March 2019 and March 2023, 10 patients with huge fat pad on the nape and back were treated. There was 1 male and 9 females with an average age of 52 years (range, 39-57 years). All patients had soft tissue bulge on the nape and back. Preoperative MRI showed the subcutaneous fat thickening. The length of the longitudinal axis of the fat pad ranged from 10.0 to 25.0 cm (mean, 14.1 cm), the length of the transverse axis ranged from 6.0 to 15.0 cm (mean, 10.8 cm); the thickness of the fat pad ranged from 2.5 to 5.1 cm (mean, 3.9 cm). Under general anesthesia, the patient was placed in a prone position and a hairpin shaped incision was made. The flap was lifted to remove the fat pad according to the marked area. The dressing was changed every 2 days after operation. Results: The operation time was 35-110 minutes (mean, 72 minutes). The intraoperative blood loss was 35-80 mL (mean, 49.5 mL). The drainage tube was removed at 2-5 days after operation (mean, 3.4 days). All incisions healed by first intention without incision dehiscence, infection, subcutaneous bruising, hematoma, or other related complications. All patients were followed up 2-24 months (mean, 12 months). All patients had a good shape of the nape and back and no noticeable scar on the incision. According to the Vancouver Scar Scale evaluation criteria, the incision scar score was 3-5 (mean, 3.7) at 2 months after operation. Patients had good neck movement with no recurrence. Conclusion: For the huge fat pad on the nape and back, the plastic surgery using hairpin shaped incision and cover-lifting flap has the advantages of fully exposing the fat pad, concealed incision, simple operation, and natural shape of the nape and back after operation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".