C‐reactive protein is an informative predictor of renal cell carcinoma‐specific mortality
Bibliographic record
Abstract
BACKGROUND: C-reactive protein (CRP) represents a promising prognostic variable in patients with sporadic renal cell carcinoma (RCC). It was hypothesized that CRP can improve the prognostic ability of standard RCC-specific mortality (RCC-SM) predictors in patients treated with nephrectomy for all stages of RCC. METHODS: Radical nephrectomy was performed in 314 patients from 2 European centers. Life table, Kaplan-Meier, and Cox regression analyses addressed RCC-SM. Covariates included age, gender, TNM stage, tumor size, Fuhrman grade, and histologic subtype. RESULTS: The median survival of the cohort was 19.9 years. Age ranged from 10 to 77 years. Most patients were male (69%). T-stages were distributed as follows: T1-121 (38.7%), T2-45 (14.4%), T3-140 (44.7%), T4-7 (2.2%). CRP values ranged from 1.0 to 358.0 mg/L (mean 40.9, median 11.0 mg/L). In multivariable analyses, CRP was an independent predictor of RCC-SM (P = .003). The consideration of CRP in the multivariable model increased the predictive accuracy by 3.7% (P < .001). Moreover, the model with CRP performed 2.4% and 4.6% better than the UCLA Integrated Staging System (UISS) at, respectively, 2 and 5 years. CONCLUSIONS: CRP represents an informative predictor of RCC-SM. Its routine use could allow better risk stratification and risk-adjusted follow-up of RCC patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".