Prevalence and Clinical Significance of Potential Drug–Drug Interactions in Hospitalized Pediatric Oncology Patients: A Prospective Pharmacoepidemiologic Study
Bibliographic record
Abstract
BACKGROUND: Drug-drug interactions (DDIs) are frequent and potentially harmful in pediatric cancer patients due to polypharmacy and complex chemotherapy regimens. However, data on DDIs in hospitalized pediatric oncology patients remain limited, particularly in Middle Eastern settings. METHODS: In this prospective study, we analyzed prescriptions for hospitalized pediatric oncology patients in Iran to assess the prevalence, severity, and nature of potential DDIs (PDDIs). Chemotherapy and supportive medications were analyzed using two validated databases (Lexi-Interact™ and Drugs.com™) between November 2019 and June 2020. RESULTS: Of 80 patients (median age 8.9 years), 21.2% had at least one documented PDDI. We identified 197 total PDDIs involving 42 unique drug pairs. The most common DDIs included acetaminophen and granisetron (severity rating: moderate). Methotrexate and vincristine were the most frequent antineoplastic DDI pair. Methotrexate alone accounted for 156 interactions. CONCLUSIONS: This is the first prospective study from Iran-and the largest in the region-investigating PDDIs in pediatric oncology. The dual-database screening approach improved PDDI detection. Clinical teams should routinely evaluate medication profiles in pediatric cancer patients to minimize avoidable harms from DDIs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".