[Association of prognosis with insulin-like growth factor receptor type I expression in gastric cancer patients: a meta-analysis].
Bibliographic record
Abstract
OBJECTIVE: To systemically evaluate the relationship between the expression of insulin-like growth factor receptor type I (IGF-1R) and prognosis in gastric cancer (GC) patients. METHODS: A literature search was conducted from PubMed, EMBASE, Web of Science, CNKI, Wanfang and VIP databases to retrieve the clinical studies relevant to IGF-1R expression and its prognostic value in GC patients. Meta-analysis was performed using STATA 12.0 software. The methodology was assessed according to the European Lung Cancer Working Party Quality Scale for Biological Prognostic Factors for Lung Cancer. The quality of studies was assessed using the Newcastle-Ottawa scale. RESULTS: Four eligible studies including 685 patients were enrolled for this meta-analysis. Analysis results suggested that up-regulation of IGF-1R in GC patients was significantly associated with TNM staging (OR=5.20, 95%CI:1.12 to 24.15, P=0.035), lymph node metastasis(OR=8.24, 95%CI:2.68 to 25.34, P=0.000) and distant metastasis(OR=17.34, 95%CI:6.52 to 46.15, P=0.000). Moreover, up-regulated IGF-1R expression was significantly associated with poor overall survival of gastric cancer patients(HR=2.63, 95% CI:1.29 to 5.40, Z=2.64, P=0.008). CONCLUSION: High IGF-1R expression may be an adverse prognostic factor in gastric cancer patients.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.049 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".