Interaction Between Low-Dose Methotrexate and Nonsteroidal Anti-inflammatory Drugs, Penicillins, and Proton Pump Inhibitors
Bibliographic record
Abstract
OBJECTIVE: To review the potential drug interactions between low-dose methotrexate (LD-MTX) and nonsteroidal anti-inflammatory drugs (NSAIDs), penicillins, and proton-pump inhibitors (PPIs) given the disparity between interactions reported for high-dose and low-dose MTX to help guide clinicians. DATA SOURCES: A literature search was performed in MEDLINE (1946 to September 2016), EMBASE (1974 to September 2016), and International Pharmaceutical Abstracts (1970 to January 2015) to identify reports describing potential drug interactions between LD-MTX and NSAIDS, penicillins, or PPIs. Reference lists of included articles were reviewed to find additional eligible articles. STUDY SELECTION AND DATA EXTRACTION: All English-language observational, randomized, and pharmacokinetic (PK) studies assessing LD-MTX interactions in humans were analyzed to determine clinical relevance in making recommendations to clinicians. Clinical case reports were assigned a Drug Interaction Probability Scale score. DATA SYNTHESIS: A total of 32 articles were included (28 with NSAIDs, 3 with penicillins, and 2 with PPIs [1 including both PPI and NSAID]). Although there are some PK data to describe increased LD-MTX concentrations when NSAIDs are used concomitantly, the clinical relevance remains unclear. Based on the limited data on LD-MTX with penicillins and PPIs, no clinically meaningful interaction was identified. CONCLUSION: Given the available evidence, the clinical importance of the interaction between LD-MTX and NSAIDs, penicillins, and PPIs cannot be substantiated. Health care providers should assess the benefit and risk of LD-MTX regardless of concomitant drug use, including factors known to predispose patients to MTX toxicity, and continue to monitor clinical and laboratory parameters per guideline recommendations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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".