Drug interactions in ambulatory cancer patients receiving cancer-directed therapy
Bibliographic record
Abstract
6129 Background: Cancer patients (pts) are susceptible to drug interactions (DI) because they receive multiple medications. Here we evaluate the epidemiology of potential DI in cancer patients. Methods: Ambulatory, adult pts with solid malignancies who were receiving antineoplastic treatment completed a questionnaire about drugs taken in the previous month. Drug Interactions Facts software was used to screen for potential DI and to classify them in terms of severity (major, moderate and minor, where major is a life-threatening interaction) and level of scientific evidence (1 to 5, with 1 being the highest level). Summary statistics and logistic regression were used to describe the results. Results: Between Sept -Dec 2005, 183 pts completed the questionnaire: median age was 61 (range 26–88), and 114 (62%) were women. Cancer sites included breast 63 (35%), gastrointestinal 45 (25%), and genitourinary 36 (20%). Treatment intent was palliative in 139 (60%). Most pts (76%) were receiving chemotherapy; 111 (66%) had at least one co-morbid condition, and 72 (47%) had abnormal liver and/or renal function. The median number of drugs per pt was 5 (range 0–16). Among the 183 pts, 131 potential DI were identified of which 65 (50%) were due to pharmacokinetic interactions. Sixteen (12%) of all potential DI were major, 94 (72%) were moderate, and 21 (16%) were minor. Fourteen (11%), 54 (41%), 5 (4%), 42 (32%), and 16 (12%) were supported by levels 1,2,3,4,5 of evidence respectively. Most potential DI involved non-chemotherapy agents such as warfarin, antihypertensives, and anti-inflammatory drugs. In multivariate analyses, number of comorbidities (p= .0001), number of drugs (p= .005), and cancer type (p= .03) were significant risk factors for DI. Conclusion: PotentialDI are common in oncology, and usually involve non-chemotherapeutic drugs. Risk factors for potential DI include comorbid illness, number of medications and type of cancer. Oncologists should be aware of such an important issue. No significant financial relationships to disclose.
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".