Simple prognostic score for metastatic castration‐resistant prostate cancer with incorporation of neutrophil‐to‐lymphocyte ratio
Bibliographic record
Abstract
BACKGROUND: The neutrophil-to-lymphocyte ratio (NLR), a marker of inflammation, has been reported to be a poor prognostic indicator in prostate cancer. Here we explore the use of the NLR to establish a simple prognostic score for men with metastatic castration-resistant prostate cancer (mCRPC) treated with docetaxel. METHODS: In the training cohort, the NLR and other known prognostic variables were evaluated among a cohort of chemotherapy-naïve patients treated with thrice-weekly docetaxel at the Princess Margaret Cancer Centre. Significant prognostic variables identified by univariable Cox regression were evaluated by the area under the receiver operating characteristic curves. Multivariable Cox regression was then used to derive a prognostic score where 1 risk point was assigned for each significant variable. The model was externally validated in a cohort of patients treated at the Royal Marsden. RESULTS: Three hundred fifty-seven patients were analyzed in the training cohort. Median age was 71 years, 12% had liver metastasis, and median overall survival (OS) was 14.7 months. Liver metastases, hemoglobin <12 g/dL, alkaline phosphatase >2.0× upper limit of normal (ULN), lactate dehydrogenase >1.2× ULN, and NLR >3 were associated with significantly worse OS in multivariable analysis. Four risk categories were subsequently established with 0, 1, 2, and 3-5 points. Two-year OS rates for these categories were 43%, 37%, 12%, and 3%, respectively. Area under the curve for the training cohort was 0.78 (95% CI, 0.72-0.84) compared with 0.66 (95% CI, 0.58-0.74) for the 215 patients in the validation cohort. CONCLUSIONS: This simple risk score provides good prognostic and discriminatory accuracy for men with mCRPC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".