Renal Function Deterioration in Postoperative (Adjuvant) Chemotherapy for Colon Cancer—Real-Life Data
Bibliographic record
Abstract
The knowledge concerning mild-to-moderate renal toxicity of adjuvant chemotherapy (CTH) in colon cancer patients is scarce. We retrospectively evaluated changes in the estimated glomerular filtration rate (eGFR) after three months of adjuvant treatment and the overall renal risk of the 6-month regimen in 145 patients who completed three months of therapy at three oncological centers. A decrease in eGFR of at least 1.5 mL/min/1.73 m2 after three months and 3.0 mL/min/1.73 m2 after six months was considered relevant in terms of kidney-related cardiovascular risk. Out of 114 patients who completed a 6-month regimen, kidney function deterioration occurred in 62 (54.4%) after 3 months and in 54 (47.4%) after 6 months. Age ≥ 70 years (RR = 2.66; 95% CI: 1.15–6.16) and diabetes (RR = 2.52; 95% CI: 0.98–6.45) were risk factors for kidney outcomes during the first three months of CTH. However, renal function decline during the first three months did not increase the risk of further deterioration on CTH continuation. In conclusion, older age and diabetes are factors increasing the risk of renal function deterioration during adjuvant CTH in colon cancer patients without preexisting chronic kidney disease. However, the decline during the first three months does not allow for predicting further changes under continued adjuvant 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 | 0.005 |
| 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 | 0.001 | 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".