C‐reactive protein as a prognostic marker for men with androgen‐independent prostate cancer
Bibliographic record
Abstract
BACKGROUND: Studies of cancer risk and molecular carcinogenesis suggest a role for inflammation in cancer development and progression. The authors sought to determine whether specific blood proteins associated with inflammation predict for outcomes in men with metastatic androgen-independent prostate cancer (AIPC) who are initiating docetaxel-based chemotherapy. METHODS: Baseline plasma samples were stored (-80 degrees C) from 160 of 250 patients enrolled in the AIPC Study of Calcitriol ENhancing Taxotere (ASCENT) trial, a randomized, placebo-controlled trial comparing weekly docetaxel plus high-dose calcitriol with weekly docetaxel. Multiplex immunoassays measured 16 cytokine, chemokine, cardiovascular, or inflammatory markers. The Cox proportional hazards model was used to assess associations between baseline biomarkers, clinical characteristics, and survival. Logistic regression was used for analyses of associations with prostate-specific antigen (PSA) decline. RESULTS: C-reactive protein (CRP) was found to be significantly predictive of a shorter overall survival (hazards ratio [HR] of 1.41 for each natural logarithm [ln] [CRP] increase; 95% confidence interval [95% CI], 1.20-1.65 [P < .0001]). When CRP (continuous) was entered into a multivariate model using 13 baseline clinical variables, only elevated CRP remained a significant predictor (P < .0001) of shorter overall survival. When categorized as normal ( 8 mg/L), elevated CRP was found to be a significant predictor of shorter overall survival (HR of 2.96; 95% CI, 1.52-5.77 [P = .001]), as was hemoglobin (P = .007). Elevated CRP was also associated with a lower probability of PSA decline (odds ratio of 0.74 for each ln(CRP) increase; 95% CI, 0.60-0.92 [P = .007]). CONCLUSIONS.: Elevated plasma CRP concentrations appear to be a strong predictor of poor survival and lower probability of PSA response to treatment in patients with AIPC who are receiving docetaxel-based therapy.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".