Neutrophil/lymphocyte ratio (NLR) as a prognostic factor in biliary tract cancer (BTC).
Bibliographic record
Abstract
4130 Background: BTCs include intrahepatic (IHC), hilar, distal bile duct (DBD), and gallbladder carcinoma (GBC). Risk factors include conditions associated with chronic inflammation. NLR, an inflammatory marker, is prognostic in several cancers but has not been reviewed in large BTC series, hilar or GBC. Methods: Baseline demographics and NLR at diagnosis were evaluated in 864 patients (pts) with BTC from 01/87 - 12/12 treated at Princess Margaret Cancer Center. Their prognostic significance for overall survival (OS) was determined using a Cox proportional hazards model. Results: High NLR ≥3.0 was associated with poor survival using univariable analysis as was stage/site of primary (P<0.05), age >65yrs, lymphocytes ≤1.6 (P<0.01), neutrophils ≥5.0, platelets ≥280, hemoglobin (Hb) < 110 g/L (P<0.001). Median OS in pts with NLR<3.0 was 21.6 mo, 12.0 mo with NLR ≥3.0 (P<0.001). NLR retained its significance as a prognostic marker on multivariable analysis (Table), along with GBC (P<0.05), age>65yrs, DBD primary (P<0.01), stage and Hb <110g/L (P<0.001). NLR was prognostic for OS on multivariable analysis for hilar: overall (Table) and advanced grp (n=102) (HR 1.68, 95%CI 1.07-2.64, P<0.05) and in advanced DBD (n=102) (HR 1.63, 95%CI 1.03-2.57,P<0.05). On subgrp analysis, NLR was prognostic for OS in advanced BTC (ABTC) (n=538) (P<0.01) but not in surgical grp. NLR did not predict RECIST response to first line palliative chemotherapy in ABTC. Conclusions: Baseline NLR is prognostic in BTC, specifically ABTC and hilar subgrp, suggesting the importance of systemic inflammation influencing outcome in pts with ABTC, thus providing a simple inexpensive prognostic biomarker while also possibly identifying pts that may benefit from antiinflammatory mediation. NLR was not predictive for response in BTC. [Table: see text]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".