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Record W4400787428 · doi:10.7717/peerj.17585

The prognostic role of lymphocyte-to-monocyte ratio in patients with resectable pancreatic cancer: a systematic review and meta-analysis

2024· review· en· W4400787428 on OpenAlexaboutno aff
Haipeng Li, Shang Peng, Ran An, Nana Du, Huan Wu, Xiangcheng Zhen, Yuanzhi Gao, Zhenghong Li, Jingting Min

Bibliographic record

VenuePeerJ · 2024
Typereview
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
FundersAnhui Provincial Department of Science and Technology
KeywordsMedicineHazard ratioInternal medicinePancreatic cancerConfidence intervalMeta-analysisSubgroup analysisOncologyGastroenterologyPopulationCancer

Abstract

fetched live from OpenAlex

Objectives This systematic review and meta-analysis examined whether the lymphocyte-to-monocyte ratio (LMR) can serve as an indicator for predicting the prognosis of patients with resectable pancreatic cancer. Patients and Methods This meta-analysis was registered with PROSPERO: CRD42023461260. A systematic literature search was conducted in the PubMed, Embase, Cochrane, and Web of Science databases up to September 2023 to assess whether LMR can predict the prognosis of patients with resectable pancreatic cancer. The outcomes measured included subgroup analyses of overall survival (OS) with hazard ratios (HR) and confidence intervals of geographical region, patient population, and LMR threshold. A sensitivity analysis was also performed for OS and HR and confidence intervals were calculated for recurrence-free survival (RFS). Results A total of 14 eligible articles, comprising 4,019 patients, were included in the comprehensive analysis. The results of this comprehensive analysis indicate that LMR is a robust predictor of OS, demonstrating strong prognostic significance (HR = 0.55, 95% CI [0.44–0.69], I2 = 79%, P < 0.00001). This predictive significance extended to various types of pancreatic cancer, such as pancreatic ductal adenocarcinoma (HR = 0.73, 95% CI [0.57–0.93], I2 = 46%, P = 0.01), pancreatic neuroendocrine neoplasms (HR = 0.81, 95% CI [0.66–0.99], P = 0.04) and other subtypes (HR = 0.40, 95% CI [0.22–0.72], I2 = 89%, P < 0.00001), but not to pancreatic head cancer (HR = 0.46, 95% CI [0.16–1.13], I2 = 59%, P = 0.12). LMR retained its predictive value across different regions, including Asia (HR = 0.62, 95% CI [0.47–0.76], I2 = 68%, P < 0.0001), Europe (HR = 0.78, 95% CI [0.67–0.91], I2 = 0%, P = 0.002), and the Americas (HR = 0.14, 95% CI [0.08–0.24], I2 = 0%, P < 0.00001). Notably, both LMR cut-off values greater than or equal to three (HR = 0.62, 95% CI [0.47–0.82], I2 = 67%, P = 0.0009) and less than three (HR = 0.47, 95% CI [0.32–0.69], I2 = 85%, P = 0.0001) exhibited prognostic significance. The sensitivity analysis for OS confirmed the strong predictive value of LMR, whereas LMR did not exhibit predictive significance for RFS (HR = 0.35, 95% CI [0.09–1.32], I2 = 95%, P = 0.12). In both subgroups categorized by Newcastle-Ottawa Scale (NOS) scores of ≥7 (HR = 0.66, 95% CI [0.54–0.80], I2 = 53%, P = 0.04) and <7 (HR = 0.41, CI [0.23–0.72], I2 = 89%, P < 0.00001), LMR was demonstrated to have predictive value. Conclusion Despite the observed heterogeneity and potential biases in the included studies, the findings of this study suggest that LMR may serve as a valuable predictor of OS in patients with resectable pancreatic cancer.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.028
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.036
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.030
GPT teacher head0.326
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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