Potential Drug Interactions in Patients with a History of Cancer
Bibliographic record
Abstract
BACKGROUND: Cancer survivors (css) are frequently exposed to polypharmacy, which might increase their risk of drug interactions. Our study aimed to determine the relative prevalence of potential drug interactions (pdis) among css compared with non-cancer respondents (ncrs). METHODS: Self-reported prescription data from 4975 patients were extracted from the U.S. National Health and Nutrition Examination Survey and screened for pdis using iFacts: Drug Interaction Facts (Facts and Comparisons, St. Louis, MO, U.S.A.). The clinical significance of each pdi was graded on a 5-point scale based on the severity of the interaction and the level of evidence documenting the interaction. Summary statistics and logistic regression models were used to assess the impact of cancer history on the risk of pdis. RESULTS: Of patients eligible for the analyses, the css (n = 302) indicated using 4.4 ± 0.22 prescriptions on average, and the ncrs (n = 908), 3.8 ± 0.09. Nearly half of both cohorts (40% of css, 43% of ncrs) had at least 1 pdi. In both cohorts, 12% were at risk for fatal or permanently debilitating effects. In multivariate analyses, css were significantly less likely than ncrs to be at risk for any pdis (odds ratio: 0.65; 95% confidence interval: 0.46 to 0.92; p = 0.02). Advanced age and low household income were associated with pdis among css. Medications most commonly prescribed to css with a pdi included metoprolol (15.6%), levothyroxine (13.6%), and furosemide (11.9%). CONCLUSIONS: Although css appear to be less susceptible than ncrs to pdis, the prevalence of pdis among css remains suboptimal. Specific subgroups of css may be particularly prone to pdis, underscoring the importance of increased vigilance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".