Prognostic value of the HALP score in breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The present research is a meta-analysis aimed at quantifying the influence of the hemoglobin, albumin, lymphocyte, and platelet (HALP) score on the overall survival (OS) and disease-free survival (DFS) in breast cancer patients. The objective of this research is to examine the prognostic significance of the HALP score in breast cancer patients and evaluate its predictive performance for survival outcomes. Methods: This research was conducted adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We retrieved PubMed, EMBASE, Web of Science, Cochrane Library, and Google Scholar for studies relevant to the link between the HALP score and the prognosis of breast cancer up to July 3, 2025. The quality of the studies was appraised with Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and the Newcastle-Ottawa Scale (NOS). MetaDiSc 1.4, Stata SE 18, and Review Manager 5.4 were leveraged to examine the risk of bias and to conduct statistical analysis. Results: Among 971 studies, nine fulfilled the inclusion criteria, encompassing a total of 4,560 individuals. An increased HALP score was linked to favorable OS (HR = 0.61, 95% CI 0.39-0.97, p < 0.05). No statistically significant association was noted between the HALP score and DFS in breast cancer patients. The area under the summary receiver operating characteristics (SROC) curve (AUC) of the HALP score for predicting pathological complete response (pCR) was 0.57. Conclusion: Elevated HALP scores may represent a prognostic biomarker for favorable survival outcomes in breast cancer patients. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/myprospero identifier CRD420251031999.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".