Digoxin Use After Diagnosis of Colorectal Cancer and Survival: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Digoxin has been shown to affect a number of pathways that are of relevance to cancer, and its use has been associated with increased risks of breast and uterus cancer and, more recently, a 40% increase in colorectal cancer risk. These findings raise questions about the safety of digoxin use in colorectal cancer patients, and, therefore, we investigated whether digoxin use after colorectal cancer diagnosis increased the risk of colorectal cancer-specific mortality. METHODS: A cohort of 10,357 colorectal cancer patients newly diagnosed from 1998 to 2009 was identified from English cancer registries and linked to the UK Clinical Practice Research Datalink (to provide digoxin and other prescription records) and to the Office of National Statistics mortality data (to identify 2,724 colorectal cancer-specific deaths). Using time-dependent Cox regression models, unadjusted and adjusted HRs and 95% confidence intervals (CI) were calculated for the association between postdiagnostic exposure to digoxin and colorectal cancer-specific mortality. RESULTS: Overall, 682 (6%) colorectal cancer patients used digoxin after diagnosis. Digoxin use was associated with a small increase in colorectal cancer-specific mortality before adjustment (HR, 1.25; 95% CI, 1.07-1.46), but after adjustment for confounders, the association was attenuated (adjusted HR, 1.10; 95% CI, 0.91-1.34) and there was no evidence of a dose response. CONCLUSIONS: In this large population-based colorectal cancer cohort, there was little evidence of an increase in colorectal cancer-specific mortality with digoxin use after diagnosis. IMPACT: These results provide some reassurance that digoxin use is safe in colorectal cancer patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".