Potential Drug Interactions and Duplicate Prescriptions Among Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Cancer patients receive numerous medications, including antineoplastic agents, drugs for supportive care, and medications for comorbid illnesses. Therefore, they are at risk for drug interactions and duplicate prescribing. METHODS: A questionnaire eliciting information on demographics and medications taken in the previous 4 weeks was given to adult outpatients receiving systemic anticancer therapy for solid tumors. The Drug Interaction Facts software, version 4.0, was used to identify potential drug interactions and to classify them by level of severity (major, moderate, or minor) and the strength of scientific evidence for them (using categories [1-5] of decreasing certainty). Summary statistics and logistic regression were used to analyze the data. All statistical tests were two-sided. RESULTS: The survey was completed by 405 patients. We observed 276 potential drug interactions, and at least one potential interaction was identified in 109 patients (27%; 95% confidence interval [CI] = 23% to 31%). Of the potential interactions, 25 (9%) were classified as major and 211 (77%) as moderate. Nearly half (49%) of potential interactions were supported by level 1 or 2 scientific evidence. Most potential drug interactions (87%) involved non-anticancer agents such as warfarin, antihypertensive drugs, corticosteroids, and anticonvulsants, but some (n = 36, 13%) involved antineoplastic agents. In multivariable analysis, increased risk of receiving drug combinations in which there were potential drug interactions was associated with receipt of increasing numbers of drugs (odds ratio [OR] = 1.4 per additional drug, 95% CI = 1.26 to 1.58, P<.001 from the Wald chi-square test), type of medication (drugs to treat comorbid conditions versus supportive care medications only; OR = 8.6, 95% CI = 2.9 to 25, P<.001), and the presence of brain tumors. Thirty-two (8%) patients were exposed to duplicate medications, most often corticosteroids, proton pump inhibitors, or benzodiazepines. CONCLUSION: Potential drug interactions were common among cancer patients and most often involved medications to treat comorbid conditions. Duplicate medications were infrequent.
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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".