Study on the Cosmetic Effect of Dot Matrix CO2 Laser Combined with Botulinum Toxin Type A of Facial and Neck Hypertrophic Scar
Bibliographic record
Abstract
目的:探讨点阵CO2激光联合A型肉毒毒素治疗面颈部增生性瘢痕的美学效果.方法:选取2016年5月-2018年7月笔者医院收治的面颈部增生性瘢痕患者作为研究对象,共116例.采用随机数字表法将其分为对照组(58例)和研究组(58例),其中对照组采用点阵CO2激光进行治疗,治疗2周,研究组采用CO2激光联合A型肉毒毒素进行治疗,治疗10周.比较两组治疗前后的面颈部瘢痕厚度、温哥华瘢痕量表(Vancouver scar scale,VSS)评分、转化生长因子-β1(Transforming growth factor-β1,TGF-β1)水平;对比两组治疗后的视觉模拟评分(Visual analogue score,VAS)、完全上皮化时间以及不良反应发生率.结果:治疗后,两组瘢痕厚度、VSS评分、TGF-β1水平均显著下降,且研究组均低于对照组,差异有统计学意义(P 0.05).结论:点阵CO2激光联合A型肉毒毒素治疗面颈部增生性瘢痕疗效显著,能够明显改善患者瘢痕情况,降低TGF-β1水平,缓解患者疼痛症状.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".