Expression and clinical significance of survivin in ovarian cancer: A meta-analysis
Bibliographic record
Abstract
To assess the clinicopathological significance of survivin in ovarian carcinoma through this meta-analysis. PubMed, EMBASE, Web of Science, and The Cochrane Library databases were searched for relevant studies published through September, 2017. Included studies reported the case-control study of surviving expression with ovarian cancer and its clinicopathological characteristics. The quality assessment was performed according to the Newcastle-Ottawa Scale (NOS) for quality assessment of case-control studies. Statistical analysis was performed with the software Stata 12.0. Twelve eligible studies with a total of 1097 patients were included in this meta-analysis. Survivin overexpression was closely related to FIGO stage (I-II vs. III-IV) of ovarian carcinoma (odds ratio [OR] = 0.26,95% confidence interval [CI]:0.16,0.42),P<0.00001),tumor grade (G1-G2 vs. G3) (OR = 0.29,95%CI(0.17, 0.51),P <0.0001), but was not significantly associated with lymphatic metastasis (OR = 1.53, 95%CI(0.77, 3.03, P = 0.23),ascites (OR = 0.89,95%CI(0.39,2.05),P = 0.79). Our meta-analysis shows that survivin is strongly associated with FIGO stage and tumor grade of ovarian carcinoma. Maybe survivin is a novel clinicopathological marker of ovarian carcinoma.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.057 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".