Baseline Edmonton Symptom Assessment System and Survival in Metastatic Renal Cell Carcinoma
Bibliographic record
Abstract
Background: Baseline symptom burden as measured using the Edmonton Symptom Assessment System (ESAS), a patient-reported, validated, and reliable tool measuring symptom severity in 9 separate domains, might yield prognostic information in patients receiving treatment for metastatic renal cell carcinoma (MRCC) and might add to the existing prognostic models. Methods: In this retrospective single-centre cohort study, we included patients receiving first-line sunitinib therapy for MRCC between 2008 and 2012. Baseline variables included information relevant to the pre-existing prognostic models and pre-treatment ESAS summation scores (added together across all 9 domains), with higher scores representing greater symptom burden. We used Kaplan–Meier curves and Cox regression modelling to determine if symptom burden can provide prognostic information with respect to overall survival. Results: We identified 68 patients receiving first-line therapy for MRCC. Most had intermediate- or poor-risk disease based on both the Memorial Sloan Kettering Cancer Center (MSKCC) and the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) models. The median baseline ESAS summation score was 16 (range: 6–57). In univariable analysis, the hazard ratio for overall survival was 1.270 (p = 0.0047) per 10-unit increase in summation ESAS. In multivariable analysis, the hazard ratio was 1.208 (p = 0.0362) when controlling for MSKCC risk group and 1.240 (p = 0.019) when controlling for IMDC risk group. Conclusions: Baseline symptom burden as measured by ESAS score appears to provide prognostic information for survival in patients with mrcc. Those results should encourage the investigation of patient-reported symptom scales as potential prognostic indicators for patients with advanced cancer.
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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 | 0.000 |
| 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".