The Prognostic Value of the Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score in Lung Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Lung cancer remains the leading cause of global cancer mortality. The HALP (hemoglobin, albumin, lymphocyte, platelet) score integrates nutritional, immune, and inflammatory status and may offer prognostic value. This meta-analysis evaluates the association between the HALP score and survival outcomes in lung cancer patients. Methods: Following PRISMA guidelines, PubMed, Embase, Web of Science, CNKI, Wanfang, and Google Scholar were searched. Inclusion criteria covered observational studies in lung cancer reporting hazard ratios (HRs) for overall survival (OS), progression-free survival (PFS), or disease-free survival (DFS). Study quality was assessed via the Newcastle–Ottawa Scale (NOS). Random-effects models were used to pool HRs (95% confidence intervals [CIs]), with subgroup and sensitivity analyses used to address heterogeneity. Results: Fourteen studies (N = 10,182 patients) were included. A high HALP score predicted significantly improved OS in multivariate analysis (HR = 0.56, 95% CI: 0.46–0.69, p < 0.001), representing a 44% mortality risk reduction. The results were consistent for surgical (HR = 0.60, CI: 0.43–0.84), advanced (HR = 0.47, CI: 0.32–0.69), and all-stage subgroups. High HALP also correlated with superior PFS (multivariate HR = 0.56, CI: 0.39–0.78, p = 0.001) but not DFS (HR = 0.50, CI: 0.22–1.16, p = 0.107). Significant heterogeneity persisted (I2 > 75% for OS), likely due to stage variability and non-standard HALP cutoffs. Publication bias was detected for OS studies (Egger′s p = 0.003). Conclusions: The HALP score is a low-cost, accessible prognostic biomarker for lung cancer. A high HALP score independently predicts better OS and PFS but not DFS, suggesting utility for long-term risk stratification. Standardized HALP thresholds and validation in diverse populations are needed for clinical implementation.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".