Effects of Programmed Death-Ligand 1 Expression and Clinicopathological Features on the Survival Outcomes of Patients with Colorectal Cancer: A Meta-Analysis.
Bibliographic record
Abstract
OBJECTIVE: To explore the effects of programmed death-ligand 1 (PD-L1) and clinicopathological features on the outcomes of patients with colorectal cancer (CRC), and to predict their prognosis using a meta-analysis. METHODS: PubMed, Web of Science, Cochrane Library, CNKI, Wanfang, and VIP databases were searched from their inception until 10 September 2023. Eligible studies that explore the relationships between PD-L1 and the survival outcomes of patients with CRC were included. The Newcastle-Ottawa scale (NOS) was used for quality assessment; combined hazard ratios (HR) for overall survival (OS), relapse-free survival (RFS) and disease-free survival were calculated. RESULTS: Eighteen studies (involving 6,160 patients) with an NOS score of >7 were included in this study. The PD-L1 expression was associated with worse OS (HR=1.45; 95% CI: 1.08-1.94) and RFS (HR=1.87; 95% CI: 1.31-2.67). Male gender, poor differentiation, lymph node metastasis, Tumour-Node-Metastasis stage, pathological nodal status, pathological tumour status and vascular invasion were identified as important risk factors for worse survival outcomes in patients with CRC. CONCLUSION: The PD-L1 expression and other important clinicopathological features might be biomarkers for worse prognoses in patients with CRC.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.044 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".