Prognostic impact of paraneoplastic syndromes on patients with non-metastatic renal cell carcinoma undergoing surgery: Results from Canadian Kidney Cancer information system
Bibliographic record
Abstract
INTRODUCTION: The impact of paraneoplastic syndromes (PNS) on survival in patients with renal cell carcinoma (RCC) is uncertain. This study was conducted to analyze the association of PNS with recurrence and survival of patients with non-metastatic RCC undergoing nephrectomy. METHODS: The Canadian Kidney Cancer information system is a multi-institutional cohort of patients started in January 2011. Patients with nephrectomy for non-metastatic RCC were identified. PNS included anemia, polycythemia, hypercalcemia, and weight loss. Associations between PNS and recurrence or death were assessed using Kaplan-Meier curves and multivariable analysis. RESULTS: Of 4337 patients, 1314 (30.3%) had evidence of one or more PNS. Patients with PNS were older, had higher comorbidity, and had more advanced clinical and pathological tumor characteristics as compared to patients without PNS (all p<0.05). Kaplan-Meier five-year estimated recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS) were significantly worse in patients with PNS (63.7%, 84.3%, and 79.6%, respectively, for patients with PNS vs. 73.9%, 90.8%, and 90.1%, respectively, for patients without PNS, all p<0.005). On univariable analysis, presence of PNS increased risk of recurrence (hazard ratio [HR] 1.67, 95% confidence interval [CI] 1.48-1.90, p<0.0001) and cancer-related death (HR 1.85, 95% CI 1.34-2.54, p=0.0002). Adjusting for known prognostic factors, PNS was not associated with recurrence or survival. CONCLUSIONS: In non-metastatic RCC patients undergoing surgery, presence of PNS is associated with older age, higher Charlson comorbidity index score, advanced tumor stage, and aggressive tumor histology. Following surgery, baseline PNS is not strongly independently associated with recurrence or death.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".