Higher capecitabine AUC in elderly patients with advanced colorectal cancer (SWOGS0030)
Bibliographic record
Abstract
BACKGROUND: The aging process is accompanied by physiological changes including reduced glomerular filtration and hepatic function, as well as changes in gastric secretions. To investigate what effect would aging have on the disposition of capecitabine and its metabolites, the pharmacokinetics between patients ≥70 years and <60 years were compared in SWOG0030. METHODS: Twenty-nine unresectable colorectal cancer patients were stratified to either ≥70 or <60 years of age, where the disposition of capecitabine and its metabolites were compared. RESULTS: Notable increase in capecitabine area under the curve (AUC) was accompanied by reduction in capecitabine clearance in ≥70 years patients (P<0.05). No difference in 5'-deoxy-5-fluorocytidine, 5'-deoxy-5-fluorouridine (DFUR), and 5-fluorouracil (5FU) AUCs between the two age groups, suggesting that carboxylesterase and cytidine deaminase (CDA) activity was similar between the two age groups. These results suggest that metabolic enzymes involved in converting capecitabine metabolites are not altered by age. An elevation in capecitabine Cmax and reduction in clearance was seen in females, where capecitabine AUC was 40.3% higher in women. Elevation of DFUR Cmax (45%) and AUC (46%) (P<0.05) was also noted, suggesting that CDA activity may be higher in females. CONCLUSION: Increases in capecitabine Cmax and AUC was observed in patients ≥70 years when compared with younger patients who were >60 years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".