Prognostic Significance of Tumor-Stroma Ratio in Hepatocellular and Gall Bladder Carcinoma: Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: The tumor-stroma ratio (TSR) has emerged as a crucial prognostic marker in various cancers, including breast, colorectal, and lung cancers. However, evidence for its prognostic value in hepatocellular carcinoma (HCC) and gallbladder carcinoma (GBC) remains limited and inconsistent. This systematic review and meta-analysis will assess the prognostic significance of TSR in patients with HCC and GBC, determining its potential to guide clinical decision-making and improve patient outcomes. Methods: The PubMed, Scopus, and Web of Science databases will be comprehensively searched, with no language or publication date restrictions. Studies eligible for inclusion are cohort studies, and case-control studies, that evaluate the prognostic value of TSR in patients diagnosed with HCC or GBC. The TSR is defined as the proportion of stromal tissue relative to tumor cells, with a cut-off value of 50% used to categorize patients as TSR-high or TSR-low. Data extraction and quality assessment will be independently performed by three researchers, with extracted data including study details, patient demographics, and outcomes (overall survival). The quality of each study will be assessed using the Newcastle Ottawa Scale. Results: The results will be pooled in a meta-analysis, calculating hazard ratios (HR) for survival outcomes. Statistical heterogeneity will be assessed using the I², Q test, tau², and prediction intervals. Subgroup analyses and meta-regression will explore potential sources of heterogeneity, and sensitivity analyses will be conducted to test the robustness of the findings. Publication bias will be evaluated using Begg’s funnel plot and Egger’s test. Conclusion: This study will evaluate the prognostic significance of TSR in hepatocellular carcinoma and gallbladder carcinoma.
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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 | 0.053 | 0.094 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.005 |
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".