The Prognostic Value of PD-1/PD-L1 Expression on Tumor Cells and Tumor-Infiltrating Immune Cells in Patients with Colorectal Cancer: a Systematic Review and Meta-Analysis Protocol
Bibliographic record
Abstract
Abstract Aim Colorectal cancer (CRC) is one of the most common cancers in the world. However, the role of immune checkpoint molecules, especially Programmed cell death protein 1 (PD-1) and Programmed cell death-ligand 1 (PD-L1), in the progression of CRC remains unclear. This systematic review and meta-analysis will investigate the prognostic significance of PD-1/PD-L1 expression on tumor-infiltrating immune cells and tumor cells in patients with colorectal cancer. Methods This protocol has been prospectively registered in the PROSPERO (registration NO. CRD42020156233). A comprehensive electronic search on PubMed/MEDLINE, Scopus, Web of Science (WOS), Embase and ProQuest will be conducted using a combination of MeSH terms of “programmed cell death 1”, “programmed cell death ligand 1”, “colorectal” and “cancer” between 1 January 1990 and 31 March 2021 with no language limitation. Two independent reviewers will perform study selection, data extraction, and risk of bias assessment. The Newcastle-Ottawa Scale (NOS) for cohort studies will be used to assess the risk of bias. In the case of sufficient data, either random or fixed-effect models, will be used for meta-analysis. Statistical heterogeneity will be evaluated by 𝒳 2 test with the I 2 statistic. “Funnel plot”, “Begg’s statistical test”, and “Egger’s statistical test and graph” will be used to assess publication bias. Stata V.13 software will be employed for meta-analysis. Results and conclusion According to the meta-analysis of the aggregated data from the relevant primary studies, the relationship between expression of PD-1/PD-L1 and prognostic parameters, including progression-free survival and overall survival, will be reported. The results of the current study will be published in a peer-reviewed journal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.023 | 0.027 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".