Antiretroviral <scp>drug–drug</scp> interactions: A comparison of online drug interaction databases
Bibliographic record
Abstract
WHAT IS KNOWN AND OBJECTIVE: Antiretrovirals have a high drug interaction potential, which can lead to increased toxicity and/or decreased efficacy. Multiple databases are available to assess drug-drug interactions. The aim of our study was to compare interaction identification for commonly used ARVs and concomitant medications between six different online drug-drug interaction databases. COMMENT: This was a cross-sectional review using each of the following six databases: LexiComp®, Clinical Pharmacology®, Micromedex®, Epocrates®, University of Liverpool, and University of Toronto. Sixteen antiretroviral drugs and 100 of the DrugStats Database "Top 200 of 2019" list of medications were included. Each of the six databases identified a different number of actual or potential interactions. The number of interactions ranged from 211 to 283. WHAT IS NEW AND CONCLUSIONS: A variety of databases exist with inconsistent identification of actual or potential drug-drug interactions amongst them. It may be beneficial to cross-reference multiple databases prior to making decisions regarding patient care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".