Chinese herbal medicine for previous cesarean scar defect
Bibliographic record
Abstract
BACKGROUND: Previous cesarean scar defect (PCSD) is a gynecological disease that can cause bleeding after intercourse, prolonging menstrual period, intermenstrual bleeding, dysmenorrhea, and even lead to infertility. Chinese herbal medicine plays an important role in the treatment of gynecological diseases in China and East Asia. This study aims to assess the efficacy and safety of Chinese herbal medicine for PCSD. METHODS: We search the following databases: PubMed, the Cochrane Library, Chinese Biomedical Literature Database (CB), Chinese Science and Technique Journals Database (VIP), EMBASE, Chinese National Knowledge Infrastructure Database (CNKI), and the Wanfang Database. Other sources will also be searched like Google Scholar and gray literature. All databases mentioned above are searched from the start date to the latest version. Randomized controlled trials will be included which recruiting PCSD participants to assess the efficacy and safety of Chinese herbal medicines against controls (placebo or other therapeutic agents). Primary outcomes will include the size of PCSD, menstrual cycle, menstrual phase, menstrual volume, duration of disease, security index. Two authors will independently scan the searched articles, extract the data from attached articles, and import them into Endnote X8 and use Microsoft Excel 2013 to manage data and information. We will assess the risk of bias by Cochrane tool of risk of bias. Disagreements will be resolved by consensus or the participation of a third party. All analysis will be performed based on the Cochrane Handbook for Systematic Reviews of Interventions. The meta-analysis in this review will use RevMan 5.3 software. RESULTS: The study aims to evaluate the efficacy and safety of the treatment that Chinese herbal medicine for PCSD. CONCLUSION: This study of the meta-analysis could provide evidence for clinicians and help patients to make a better choice. INPLASY REGISTRATION NUMBER: INPLASY202090080.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".