Fractional CO<sub>2</sub> laser therapy for cesarean scar under the guidance of multiple evaluation methods: A retrospective study
Bibliographic record
Abstract
AIMS: laser for cesarean scar under the guidance of multiple evaluation methods. METHODS: laser therapy between January 2016 and January 2020 were retrospectively analyzed in this study. The demographic characteristics and treatment protocols, the Vancouver Scar Scale (VSS), the University of North Carolina "4P" Scar Scale (UNC4P), and the Antera3D score of all the enrolled patients were recorded. RESULTS: Altogether, 79 cesarean scar patients were involved in this study, with the average age of 28.1 years, the average scar age and length of 26.5 (range, 24-30) months and 8.5 (range, 7-11) cm, respectively. Significant improvements were observed in VSS (t = 16.75, P < .05), UNC4P (t = 15.63, P < .05), and Antera3D score (color:t = 13.19, P < .05; texture: t = 13.12, P < .05; melanin: t = 3.89, P < .05; hemoglobin: t = 2.28, P < .05). No long-term complication was reported during the follow-up visits. CONCLUSIONS: laser therapy is an effective treatment for cesarean scar. The multiple evaluation methods, including the combined application of VSS, UNC4P, and Antera3D score, can be potentially used for guiding treatment protocols and evaluating efficacy. Meanwhile, rhGM-CSF hydrogel provides another choice for laser wound management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".