Prognostic Significance of p53 in Gastric Cancer: a Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Gastric cancer is one of the frequently seen cancers in the world and it is the second most common reason for death due to cancer. The prognostic role of expression of p53 detected by immunohistochemistry in gastric cancer remains controversial. This meta-analysis aimed to explore any association between overexpression and survival outcomes. MATERIALS AND METHODS: We systematically searched for studies investigating the relationships between expression of p53 detected by immunohistochemistry and prognosis of gastric cancer patients. Study quality was assessed using the Newcastle-Ottawa Scale. After careful review, survival data were extracted from eligible studies. A meta-analysis was performed to generate combined hazard ratios for overall survival and disease-free survival. RESULTS: A total of 4.330 patients from 21 studies were included in the analysis. Our results showed tissue p53 overexpression in patients with gastric cancer to be associated with poor prognosis in terms of overall survival (HR, 1.610; 95% CI, 1.394 -5.235; p: <0.001). Pooled hazard ratio for disease free survival showed that p53 positivity or negativity were not statitistically significant (HR, 1.219; 95%CI, 0.782-1.899; p:0.382). CONCLUSIONS: The present meta-analysis indicated overexpression of p53 detected by immunohistochemistry to be associated with a poor prognosis in patients with gastric cancer.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".