Clinical significance of the advanced lung cancer inflammation index in gastrointestinal cancer patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Background The advanced lung cancer inflammation index (ALI) has been identified as a scientific and clinical priority in multiple malignancies. The aim of this study is to investigate the value of the ALI before treatment in evaluating postoperative complications (POCs) and survival outcomes in patients with gastrointestinal (GI) cancer. Methods Electronic databases including PubMed, Embase and Web of Science were comprehensively reviewed up to June 2022. The endpoints were POCs and survival outcomes. Subgroup analyses and sensitivity analyses were also performed. Results Eleven studies including 4417 participants were included. A significant heterogeneity in the ALI cut-off value among studies was observed. Patients in the low ALI group showed increased incidence of POCs (OR=2.02; 95%CI:1.60-2.57; P<0.001; I2 = 0%). In addition, a low ALI was also significantly associated with worse overall survival (HR=1.96; 95%CI: 1.58-2.43; P<0.001; I2 = 64%), which remained consistent in all subgroups based on country, sample size, tumor site, tumor stage, selection method and Newcastle Ottawa Scale score. Moreover, patients in the low ALI group had an obviously decreased disease-free survival compared to these in the high ALI group (HR=1.47; 95%CI: 1.28-1.68; P<0.001; I2 = 0%). Conclusion Based on existing evidence, the ALI could act as a valuable predictor of POCs and long-term outcomes in patients with GI cancer. However, the heterogeneity in the ALI cut-off value among studies should be considered when interpreting these findings.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".