Prognostic value of the preoperative systemic immune-inflammation index for overall survival after Surgical resection in gallbladder cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The systemic immune-inflammation index (SII) reflects the relationship between tumor-promoting inflammation and anti-tumor immunity in various solid malignancies, but the role of SII in Gallbladder cancer (GBC) has yet to be established. The aim of the systematic review and meta-analysis was to clarify the prognostic value of preoperative SII in GBC patients undergoing resection. Methods This systematic review and meta-analysis was performed following PRISMA 2020 checklist and we searched PubMed, Embase, Web of Science and Scopus from inception to November 1, 2025. Original studies with English language enrolled adults with resectable GBC undergoing surgical resection and reported preoperative SII with overall survival (OS) as a hazard ratio (HR) comparing high versus low SII. Risk of bias was assessed with the Newcastle-Ottawa Scale (NOS). Random-effects models were applied to pool HRs, and heterogeneity was summarized with I² and τ². We evaluated publication bias with visual inspection of funnel plots and Egger’s regression test. Results Seven studies (N= 2,153) met inclusion criteria. High preoperative SII was associated with significantly worse OS (pooled HR 2.17; 95% CI 1.55–2.79). with moderate heterogeneity (I² = 26.0%, τ² = 0.0285). Results were robust in leave-one-out analyses, and variability in study-specific SII cut-offs accounted for part of the heterogeneity. Certainty of evidence for the primary outcome was moderate, and all included studies were high quality. Conclusions preoperative SII is an inexpensive, available biomarker that correlated with risk in resectable GBC and is able to identify patients with more aggressive tumor biology despite Surgical surgery. Systematic review registration PROSPERO CRD420251185808
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".