Abnormal β-catenin immunohistochemical expression as a prognostic factor in gastric cancer:A meta-analysis
Bibliographic record
Abstract
AIM:To evaluate the effect ofβ-catenin immunohistochemical expression on the prognosis of gastric cancer(GC).METHODS:We searched Pubmed and Embase to identify eligible studies.The search ended on November 10,2013,with no lower date limit.The citation lists associated with the studies were used to identify additional eligible studies.We included studies reporting sufficient information to estimate the HR and 95%CI,and information to estimate the OR in the analysis of clinicopathological features.The qualities of these studies were assessed using the Newcastle-Ottawa Quality Assessment Scale.HRs and ORs and their variance were calculated and pooled using Review Manager Version 5.2.RESULTS:A total of 24 studies were identified and comprised 3404 cases.β-catenin expression was significantly correlated with poor overall survival(OS)in GC patients(HR=1.85,95%CI:1.39-2.46),but showed a significant degree of heterogeneity(I2=71%,P<0.0001).Subgroup analysis indicated that an abnormal pattern ofβ-catenin expression had an unfavorable effect on OS(HR=1.79,95%CI:1.39-2.32).However,accumulation in the nucleus or loss of membrane did not influence the survival of GC patients independently.Moreover,the combined OR ofβ-catenin indicated thatβ-catenin expression was associated with Lauren classification(OR=1.98,95%CI:1.19-3.29),lymph node metastasis(OR=2.00,95%CI:1.44-2.77),distant metastasis(OR=2.69,95%CI:1.35-5.38)and grade of differentiation(OR=2.68,95%CI:1.66-4.34).β-catenin expression did not correlate with TNM stage(OR=1.34 95%CI:0.96-1.86),the depth of invasion(OR=1.48,95%CI:0.94-2.33)or vascular invasion(OR=1.11,95%CI:0.70-1.76).CONCLUSION:Abnormalβ-catenin immunohistochemical expression may be associated with tumor progression and could be a predictive factor of poor prognosis in patients with GC.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.047 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".