er jia shuang zuo dui tang niao bing he bing fu ke zhong liu huan zhe yu hou ying xiang de meta fen xi
Bibliographic record
Abstract
目的 系统评价二甲双胍对糖尿病合并妇科肿瘤患者预后的影响。方法 计算机检索Pub Med、Embase、CNKI及万方数据库于2017年3月之前发表的关于二甲双胍对糖尿病合并妇科肿瘤患者预后影响的文献资料(无语种限制),并对综述性文章的参考文献进行二次检索。由两位评价员按照纳入与排除标准独立筛选文献、提取文献特征并以Newcastle-Ottawa scale(NOS)评分法评价文献质量。主要结局指标包括总生存期(OS)和无进展生存期(PFS)。采用STATA 12.0统计软件进行Meta分析,以风险比(HR)为效应量,各效应量以95%置信区间(95%CI)表示;采用I2检验来评估异质性,采用漏斗图及Begg和Egger检验评价文章的发表偏倚情况,采用敏感性分析判定结果的稳定性。结果共纳入16篇回顾性队列研究,文献质量为6~9分。Meta分析结果显示,二甲双胍可以提高糖尿病合并妇科肿瘤患者的OS(HR=0.71,95%CI 0.59~0.85,P=0.000)。对子宫内膜癌、卵巢癌、宫颈癌3种妇科恶性肿瘤进行亚组分析,结果显示,二甲双胍能够提高糖尿病合并子宫内膜癌(HR=0.70,95%CI 0.54~0.89,P=0.004)和糖尿病合并宫颈癌(HR=0.95,95%CI 0.90~1.00,P=0.048)患者OS。经过混杂因素控制后的结果显示,二甲双胍能提高糖尿病合并卵巢癌患者的OS(HR=0.56,95%CI 0.38~0.83,P=0.004)和PFS(HR=0.45,95%CI 0.30~0.68,P=0.000)。结论 二甲双胍对妇科肿瘤的预后有一定积极作用,有利于提高糖尿病合并子宫内膜癌及糖尿病合并宫颈癌患者的OS,而且能提高糖尿病合并卵巢癌患者的PFS和OS。
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".