Medication utilization patterns in patients with post-COVID syndrome (PCS): Implications for polypharmacy and drug–drug interactions
Bibliographic record
Abstract
BACKGROUND: Post-COVID syndrome (PCS) causes lasting symptoms like fatigue and cognitive issues. PCS treatment is nonspecific, focusing on symptom management, potentially increasing the risk of polypharmacy. OBJECTIVES: To describe medication use patterns among patients with Post-COVID Syndrome (PCS) and estimate the prevalence of polypharmacy, potential drug-drug interactions, and anticholinergic/sedative burden. METHODS: A cross-sectional analysis of baseline data from the Quebec Action for Post-COVID cohort, consisting of individuals self-identifying with persistent COVID-19 symptoms beyond 12 weeks. Medications were categorized using Anatomical Therapeutic Classification (ATC) codes. Polypharmacy was defined as using 5 or more concurrent medications. The Anticholinergic and Sedative Burden Catalog assessed anticholinergic and sedative loads. The Lexi-Interact checker identified potential drug-drug interactions, which were categorized into 3 severity tiers. RESULTS: Out of 414 respondents, 154 (average age 47.7 years) were prescribed medications related to persistent COVID-19 symptoms. Drugs targeting the nervous system were predominant at 54.5%. The median number of medications was 2, while 11.7% reported polypharmacy. Over half of the participants prescribed medications used at least 1 anticholinergic or sedative medication, and 25% had the potential risk for clinically significant drug-drug interactions, primarily needing therapy monitoring. CONCLUSIONS: Our study reveals prescription patterns for PCS, underscoring the targeted management of nervous system symptoms. The risks associated with polypharmacy, potential drug-drug interactions, and anticholinergic/sedative burden stress the importance of judicious prescribing. While limitations like recall bias and a regional cohort are present, the findings underscore the imperative need for vigilant PCS symptom management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".