Potential Drug Interactions in Terminally-Ill Cancer Patients, a Report from the Middle East
Bibliographic record
Abstract
This study aims to evaluate the epidemiology of potential drug interactions in terminally-ill cancer patients receiving exclusively supportive care. In this cross-sectional study, during a 6-month follow-up, we considered the medical record of terminally-ill cancer patients referred to palliative care at the cancer center in Isfahan, Iran. Potential drug-drug interactions (DDIs) were assessed by Lexi-Interact ver.1.1 online software. During the study period, 133 terminally-ill cancer patients were recruited. We detected 1678 DDIs with moderate or major severity levels. Among them, 330, 219, 32, 1075, and 51 interactions were categorized in B, C, D, and X drug interactions categories, respectively. One hundred and twenty-two patients (91.73%) encountered at least one potential drug-drug interaction during the end of life care. Mechanistically, most drug-drug interactions (64.5%) were pharmacodynamics. The most frequent pharmacological class of drugs responsible for DDIs were quetiapine (91 cases), oxycodone (87 cases), and sertraline (55 cases). Interaction between oxycodone and sertraline was found to be in the top 10 detected DDIs (13.7%). Our results showed that potentially moderate or major drug-drug interactions often occur among terminally-ill cancer patients and the clinical significance of DDIs should be considered meticulously in the palliative care cancer setting.
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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 | 0.001 | 0.001 |
| 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".