Mesenchymal stem cells in the treatment of Cesarean section skin scars: study protocol for a randomized, controlled trial
Bibliographic record
Abstract
BACKGROUND: Cesarean delivery has already become a very common method of delivery around the world, especially in low-income countries. Hypertrophic scars and wound infections have affected younger mothers and frustrated obstetricians for a long time. Mesenchymal stem cells (MSCs) have strong potential for self-renewal and differentiation to multilineage cells. Previous studies have demonstrated that MSCs are involved in enhancing diabetic wound healing. Therefore, this study is designed to investigate the safety and efficacy of using MSCs in the treatment of Cesarean section skin scars. METHODS: cells) groups at a 1:1:1 allocation ratio according to a randomization list, once a day for six consecutive days. Study duration will last for 6 months, comprising a 1 week run-in period and 24 weeks of follow-up. The primary aim of this trial is to compare the difference in Vancouver Scar Scale rating among the three groups at the 6th month. Adverse events, including severe and slight signs or symptoms, will be documented in case report forms. The study will be conducted at the Department of Obstetric of Southern Medical University Affiliated Maternal & Child Health Hospital of Foshan. DISCUSSION: This trial is the first investigation of the potential for therapeutic use of MSCs for the management of women's skin scar after Cesarean delivery. The results will give us an effective therapeutic strategy to combat Cesarean section skin scars, even with uterine scarring. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02772289 . Registered on 10 May 2016.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".