Prevalence of Drug–Drug Interactions in Primary Care Prescriptions in Egypt: A Cross-Sectional Retrospective Study
Bibliographic record
Abstract
In clinical practice, drug–drug interactions (DDIs) pose significant risks to a large number of patients. Consequently, healthcare providers are required to diligently identify, monitor, and effectively handle these interactions in order to enhance patient outcomes. In Egypt, DDIs are poorly addressed, with no reports for DDIs in primary care. In our cross-sectional, retrospective, observational study, we collected a total of five thousand, eight hundred and twenty prescriptions across eight major governorates in Egypt. Prescriptions were collected over a span of 15 months between 1 June 2021 and 30 September 2022. These prescriptions were analyzed for potential DDIs using the Lexicomp® drug interactions tool. The prevalence of DDIs was found to be 18%, with 22% of the prescriptions having two or more potential DDIs. Moreover, we found 1447 DDIs of categories C (monitoring therapy recommended), D (therapy modification suggested), and X (avoid combination). The most commonly interacting drugs in our study were diclofenac, aspirin, and clopidogrel, while non-steroidal anti-inflammatory drugs (NSAIDs) were the most reported therapeutic class implicated in pharmacologic DDIs. Pharmacodynamic agonistic activity was the most common mechanism of interaction. Therefore, it is crucial to conduct screenings, detect early signs, and closely monitor drug–drug interactions (DDIs) to enhance patients’ overall health outcomes, medication responses, and safety. In this regard, the clinical pharmacist assumes a vital role in implementing these preventive measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".