A Six-Gene Signature Predicts Clinical Outcome of Gastric Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND: The diverse anticancer measures display varied efficacy in different patients. Thus, appropriate therapy should be chosen for individual patients, and prognostic prediction, based on biomarkers, is a prerequisite for personalized therapy. OBJECTIVE: In this study, the prognostic model was established based on the genes that were significantly correlated with the survival time for patient death risk evaluation. METHOD: Univariate Cox proportional hazards regression analysis was utilized for screening the genes significantly correlated with the patients' survival time. Multivariate Cox proportional hazards regression analysis was utilized for establishing the model. Kaplan-Meier and ROC analyses were used for the validation of the prognostic prediction potential of the constructed model. RESULTS: ROC analysis was conducted in the training and validation datasets, and their AUROC values were 0.774 and 0.723, respectively. In comparison to the known prognostic biomarkers, our prognostic biomarker model constituted by the combination of 6 genes displayed superiority in prediction capability. CONCLUSIONS: These results indicated that our biomarker model could effectively stratify the risks in gastric adenocarcinoma patients with high prognostic prediction accuracy and sensitivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".