Prognostic value of <scp>IGFBP2</scp> in various cancers: a systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
BACKGROUND: The prognostic significance of insulin-like growth factor binding protein 2 (IGFBP2) expression has been explored in plenty of studies in human cancers. Because of the controversial results, the meta-analysis was carried out to evaluate the relevance of IGFBP2 expression with the prognosis in various tumors. METHODS: The data searched from four databases (Pubmed, Embase, Cochrane library, and Web of science) was used to calculate pooled hazard ratios (HRs) in this meta-analysis. Subgroup analyses were stratified by ethnicity, cancer type, publication year, Newcastle-Ottawa Scale score, treatments, and populations. RESULTS: Twenty-one studies containing 5560 patients finally met inclusion criteria. IGFBP2 expression was associated with lower overall survival (HR = 1.57, 95% CI = 1.31-1.88) and progression-free survival (HR = 1.18, 95% CI = 1.04-1.34) in cancer patients, but not with disease-free survival (HR = 1.50, 95% CI = 0.91-2.46) or recurrence-free survival (HR = 1.50, 95% CI = 0.93-2.40). The subgroup analyses indicated IGFBP2 overexpression was significantly correlated with overall survival in Asian patients (HR = 1.42, 95% CI = 1.18-1.72), Caucasian patients (HR = 2.20, 95% CI = 1.31-3.70), glioma (HR = 1.36, 95% CI = 1.03-1.79), and colorectal cancer (HR = 2.52, 95% CI = 1.43-4.44) and surgery subgroups (HR = 1.97, 95% CI = 1.50-2.58). CONCLUSION: The meta-analysis showed that IGFBP2 expression was associated with worse prognosis in several tumors, and may serve as a potential prognostic biomarker in 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".