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Neutrophil-to-lymphocyte ratio and survival outcomes in testicular cancer: A systematic review and meta-analysis

2024· article· en· W6910321331 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRevista del Cuerpo Médico Hospital Nacional Almanzor Aguinaga Asenjo · 2024
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsHazard ratioConfidence intervalPublication biasCohort studyMeta-analysisSurvival analysisProportional hazards modelCohort

Abstract

fetched live from OpenAlex

Background: The neutrophil-to-lymphocyte ratio (NLR) is a biomarker in inflammatory processes associated with multiple unfavorable outcomes in various diseases. This study aims to evaluate the association between NLR values and survival outcomes in patients diagnosed with testicular cancer.Methods: A systematic search was conducted in 6 electronic databases to retrieve studies evaluating NLR in patients with testicular cancer. The outcomes sought were overall survival (OS) and progression-free survival (PFS), and the effect measures were hazard ratio (HR) with a 95% confidence interval (CI). A random effects model was used for the meta-analysis. The risk of bias included in the studies was assessed according to the Newcastle–Ottawa Scale criteria. Egger test and Trim-and-fill method were used to test the publication bias among articles. Results: Six cohort studies (n= 1315) were evaluated. High NLR values are associated with a higher risk of OS (HR: 1.75; 95% CI 1.04 – 2.92, I2: 65%). However, no statistically significant association was found between NLR and PFS values. We found publication bias in the association between NLR and OS (Egger test < 0.1). This bias was corrected by using the trim-and-fill method (HR: 1.38, 95% CI 0.85 – 2.22). Conclusions: High NLR values are associated with worse OS; however, this result had publication bias, and the association was lost when this bias was corrected. Furthermore, no statistically significant association was found between NLR values and PFS.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
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 head0.020
GPT teacher head0.301
Teacher spread0.280 · 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