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Record W4409071227 · doi:10.6026/973206300210499

Characterization of clear cell - Renal cell carcinoma using neutrophil - Lymphocyte ratio

2025· article· en· W4409071227 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioinformation · 2025
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsTrinity Western UniversityWestern University
Fundersnot available
KeywordsLymphocyteMedicineNeutrophil to lymphocyte ratioNephrectomyRenal cell carcinomaPathologyCellAbsolute neutrophil countRenal veinInternal medicineKidneyBiologyToxicity

Abstract

fetched live from OpenAlex

The features of tumour in clear cell - renal cell carcinoma are evaluated using neutrophil - lymphocyte ratio for its prognosis. Hence, 186 clear cell-renal cell carcinoma patients with documented neutrophil lymphocyte ratio were obtained. Depending on the features of the lesion, patients underwent either a partial or radical nephrectomy and characteristics were studied in relation to normal or high neutrophil - lymphocyte ratio with a cut-off of 2.7. Of the 186 patients studied, 131 had a normal neutrophil lymphocyte ratio (<2.7), while 55 presented with an elevated neutrophil lymphocyte ratio (≥2.7). Elevated neutrophil lymphocyte ratio was significantly associated with both tumor size and renal vein invasion, with a p-value of less than 0.001. Thus, the neutrophil lymphocyte ratio is a valuable metric for assessing renal vein extension and predicting tumour size.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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