원저 : 전암성 병변 및 비흑색종 피부 악성종양의 모즈 미세도식 수술 후 마름모 피판술을 이용한 재건술
Bibliographic record
Abstract
Background: Recently, increasing attention in the field of dermatological surgery has been paid to treating skin cancers, including both premalignant and malignant lesions. The rhombic flap, one of the transposition flaps, is an outstanding method for reconstructing small- to medium-sized defects after skin surgery. Objective: The aim of this study was to evaluate our clinical results with the rhombic flap for reconstruction after Mohs micrographic surgery (MMS), including the cosmetic aspects, complete surgical excision, and recurrence. Methods: Between June 2010 and September 2013, 37 patients who were diagnosed with premalignant and malignant lesions on the face and extremities were treated with rhombic flaps for the reconstruction of primary cutaneous defects following lesion excisions. We reviewed the medical records and evaluated the clinical aspects and surgical treatment outcomes, and the cosmetic results were scored as excellent, good, fair, or poor. In addition, we assessed the surgical treatment outcomes using the Vancouver Scar Scale (VSS). Results: Thirty-seven patients received 37 rhombic flaps. The cosmetic results of the reconstructions were gratifying, and 28 of 37 patients (75.7%) showed good to excellent results. Specifically, the cosmetic results of the modified rhombic flaps were great, and 27 of 30 patients (90.0%) showed good to excellent results. The cosmetic results on the VSS showed a high mean score (2.9).Conclusion: Our study showed that the rhombic flap is a simple reconstruction method and provides aesthetically pleasing results. Therefore, it could be a useful option for reconstructing defects of the face and extremities. (Korean J Dermatol 2014;52(11):790∼796)
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 | 0.000 | 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) | 0.005 | 0.001 |
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".