Consumer Experiences of Mispurchase Associated with Drug Names
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several studies suggest that confusable brand names are the most common reason for the mispurchase of medicinal products, accounting for a quarter (25%) of all errors made in the administration of medicinal products. The study aims to investigate the frequency of mistakes associated with the naming of medicinal products. The research shows that consumers tend to confuse medicinal products with the same and different dosage forms. In 65.3% of cases, mistakes are made when choosing between two brand names with the same administration method. The detected mistakes are classified by criticality according to the severity of their consequences. The analysis shows that 79.07% of mispurchases due to confusable brand names are marked by a high criticality of consequences, as consumers confuse medicinal products belonging to different pharmacological groups. Patients do not receive the necessary treatment and end up taking drugs that are not indicated for them, which is especially dangerous for chronic patients. In 5.81% of cases, consumers mix up the brand names of medicinal products and dietary supplements or medical devices or confuse dietary supplements. In 15.12% of cases, consumers confuse analogous medicinal products with the same active substance.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it