PTEN expression is a prognostic marker for patients with non-small cell lung cancer: a systematic review and meta-analysis of the literature
Bibliographic record
Abstract
// Jian Xiao 1 , Cheng-Ping Hu 2 , Bi-Xiu He 1 , Xi Chen 2 , Xiao-Xiao Lu 1 , Ming-Xuan Xie 1 , Wei Li 1 , Shu-Ya He 3 , Shao-Jin You 4 , Qiong Chen 1 1 Department of Geriatrics, Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, China 2 Department of Respiratory Medicine, Xiangya Hospital of Central South University, Changsha 410008, China 3 Department of Biochemistry and Biology, University of South China, Hengyang 421001, China 4 Laboratory of Cancer Experimental Therapy, Atlanta Research and Educational Foundation (151F), Atlanta VA Medical Center, Decatur, GA 30033, USA Correspondence to: Qiong Chen, email: qiongch@163.com Keywords: non-small cell lung cancer, NSCLC, PTEN, disease free survival, DFS Received: June 07, 2016 Accepted: July 20, 2016 Published: August 05, 2016 ABSTRACT Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a known tumor suppressor in non-small cell lung cancer (NSCLC). By performing a systematic review and meta-analysis of the literature, we determined the prognostic value of decreased PTEN expression in patients with NSCLC. We comprehensively and systematically searched through multiple online databases up to May 22, 2016 for NSCLC studies reporting on PTEN expression and patient survival outcome. Several criteria, including the Newcastle-Ottawa Quality Assessment Scale (NOS), were used to discriminate between studies. In total, 23 eligible studies with a total of 2,505 NSCLC patients were included in our meta-analysis. Our results demonstrated that decreased expression of PTEN correlated with poor overall survival in NSCLC patients and was indicative of a poor prognosis for disease-free survival and progression-free survival in patients with NSCLC.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".