Increased reporting of accidental overdose with glucagon-like peptide-1 receptor agonists: a population-based study
Bibliographic record
Abstract
BACKGROUND: The use of online and/or compounding pharmacies to access glucagon-like peptide-1 receptor agonists (GLP-1 RAs) increases the risk for prescription error (e.g. accidental overdose) especially in racial, ethnic, and socioeconomic disadvantaged groups. METHODS: We sought to evaluate accidental overdose associated with GLP-1 RAs submitted to the United States FDA Adverse Event Reporting System (FAERS). Case reports of accidental overdose reported to the FAERS were retrieved from Q4 2003 to Q1 2024 using OpenVigil 2.1. Disproportionality of accidental overdose was assessed using reporting odds ratio (ROR). Upper and lower 95% confidence intervals (CI) were calculated at an alpha level of 5%, where disproportionate reporting was considered when the lower 95% CI was greater than 1.0. RESULTS: < 0.008), including semaglutide, dulaglutide, exenatide, liraglutide, and tirzepatide compared to niacin. CONCLUSIONS: Inadequate access, availability, and affordability of GLP-1 RAs has contributed to the increased seeking via online and/or compounding pharmacies, and is associated with greater risk for prescription errors that differentially affect racial, ethnic, and socioeconomic vulnerable populations. Pharmacovigilance database analyses cannot establish causation only association.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".