The impact of chronic kidney disease in locally advanced rectal cancer patients treated with neoadjuvant chemoradiation.
Bibliographic record
Abstract
794 Background: Chronic kidney disease (CKD) and cancer are common with advancing age. CKD may influence drug tolerance/efficacy and is an independent prognostic factor in some cancers. The impact of CKD on outcomes in patients (pts) with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiation (nCRT) has not been previously studied. Methods: We reviewed pts with LARC undergoing nCRT prior to surgery with curative intent from 2005-2013 across 4 Canadian provinces. Data regarding demographics, staging, baseline renal function, treatments and outcome were collected. CKD was defined as having an estimated glomerular filtration rate (eGFR) (Cockroft-Gault) < 60 ml/min. Primary endpoints were neoadjuvant treatment completion rate, disease-free survival (DFS), and overall survival (OS). Logistic regression and Cox proportional hazard models were used to assess for an association between renal function and outcomes. Results: 1122 (71%) of 1580 pts were included for analysis. Median age was 61 (IQR 54-69), 70% male, 84% performance status 0-1. 28% and 68% had clinical stage II and III disease, respectively. Median eGFR was 93 ml/min (IQR 74-114), with 11% < 60 ml/min (n = 120). 97% of all pts received ≥ 44 Gy (median 50 Gy [range 20-80]). 53% received 5-fluorouracil and 44% received capecitabine as neoadjuvant chemotherapy (nCT). 84% completed nCT, 95% completed neoadjuvant radiotherapy (nRT), and 76% received adjuvant chemotherapy (aCT). Pts with CKD were less likely to receive aCT (62% vs 78%; p < 0.01). There was no significant difference in completion rate of nCT (80% vs 85%; p = 0.15) or nRT (93% vs 95%; p = 0.20) based on renal function. After a median follow up time of 62 months, 8% developed local recurrence, 21% developed distant recurrence and 21% have died. 5-year OS and DFS were 78% and 73%, respectively. Pts with CKD had decreased OS on univariate analysis (HR 1.59, 95% CI 1.11-2.28; p = 0.01), but not on multivariate analysis. DFS was not significantly different based on renal function (HR 1.27, 95% CI 0.89-1.81; p = 0.18). Conclusions: In LARC pts undergoing nCRT, CKD was associated with less use of aCT but did not have any independent association with nCT and nRT completion rate, DFS or OS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".