Prognostic significance of epidermal growth factor receptor expression in glioma patients
Bibliographic record
Abstract
Purpose: There is a great controversy regarding the prognostic significance of epidermal growth factor receptor (EGFR) in glioma patients. The current meta-analysis was conducted to evaluate the effect of abnormal EGFR expression on overall survival in glioma patients. Materials and methods: A comprehensive literature search of PubMed, EMBASE, Google Scholar, Web of Science, and Cochrane Library was conducted. The combined hazard ratio (HR) and its 95% confidence intervals (CIs) were used to evaluate the association between EGFR expression and survival in glioma. Results: A total of 476 articles were screened, and 17 articles containing 1,458 patients were selected. The quality assessment of the included studies was performed by the Newcastle–Ottawa Scale. Overexpression of EGFR was found to be an indicator of poor prognosis in overall survival in glioma patients (HR =1.72, 95% CI 1.32–2.25, P =0.000, random effect) and glioblastoma multiforme patients (HR =1.57, 95% CI 1.15–2.14, P =0.004, random effect). Subgroup analysis was conducted to explore the source of high heterogeneity. Conclusion: This meta-analysis indicated that high expression of EGFR may serve as a biomarker for poor prognosis in glioma patients. Keywords: epidermal growth factor receptor, glioma, meta-analysis, prognosis
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.022 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".