MétaCan
Menu
Back to cohort

Prognostic value of neutrophil to lymphocyte ratio for gastric cancer.

2015· article· en· W333271189 on OpenAlexaboutno aff
Zhide Hu, Yuan‐Lan Huang, Bao‐Dong Qin, Qingqin Tang, Min Yang, Nan Ma, Haitao Fu, Tingting Wei, Renqian Zhong

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineNeutrophil to lymphocyte ratioConfoundingCancerOncologyRetrospective cohort studyCohort studyUnivariate analysisUnivariateMultivariate analysisLymphocyteMultivariate statistics

Abstract

fetched live from OpenAlex

BACKGROUND: Although the prognostic value of the neutrophil to lymphocyte ratio (NLR) in gastric cancer (GC) patients has been investigated by many studies, the results are heterogeneous. The objective of this systematic review is to ascertain the prognostic value of NLR in GC patients. METHODS: PubMed and Embase were retrieved to identify potential studies published before 8 June, 2014. Newcastle-Ottawa Scale (NOS) for cohort study was used to assess the quality of all eligible studies. RESULTS: Of the 20 studies included in this systematic review, 17 studies investigated the effect of NLR on overall survival (OS), 11 studies reported that NLR negatively affected OS in their multivariante analysis, and 16 studies reported that NLR negatively affected OS in univariate analysis. Three studies investigated the effect of NLR on progression-free survival (PFS), reporting that increased NLR was associated with worse PFS. Four studies investigated the effect of NLR on disease-free survival (DFS), two of which reported that increased NLR was associated with worse DFS. Two studies investigated the effect of NLR on disease special survival (DSS), but neither observed any significant association between NLR and DSS. The major design deficiencies of the studies available were retrospective data collection, inadequacy of follow-up cohorts, and unavailability of the method used for outcome assessment. CONCLUSIONS: Based on the above findings, we conclude that NLR may be a useful prognostic index (PI) for GC. In addition, future studies with prospective design, long-term follow-up and fully adjusted confounding factors are needed to rigorously assess the prognostic value of NLR for GC.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.279
Teacher spread0.236 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicInflammatory Biomarkers in Disease PrognosisFrench-language works237,207