The inadvertent consequences of drug recalls: A case study of a recall of pantoprazole generics from the markets
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
Introduction: Drug recalls may impact treatment plans or access to suitable therapies. Thus, they inadvertently affect treatment outcomes. Objective: We aimed to examine the impact of recalls on patients' safety using pantoprazole-containing products recall as a case study in terms of the occurrence of potential drug-drug interactions (pDDIs). Methods: This retrospective study used de-identified electronic health records of adult patients who had a prescription for oral proton pump inhibitors (PPIs) including pantoprazole, esomeprazole, lansoprazole, or omeprazole from April 2020 through September 2021 from a large tertiary care hospital. The study outcome definition was the prevalence of pDDIs in PPIs users before and after the recall date (March 2021). Changes in the prevalence of pDDIs were modeled using interrupted time-series. The rate ratio of pDDIs in the 12 months before and 6 months after the recall was modeled using negative binomial regression. Results: A total of 1,826 pDDIs were identified, and the median monthly prevalence of pDDI before the recall was 102.5 which increased to 115.5 after the recall. A change in the level of pDDIs occurred immediately after the recall date, followed by a gradual decrease over time. The rate of pDDIs was 69% higher after the recall compared to the baseline (rate ratio 1.69; 95% confidence interval, 0.75-1.91). Discussion: Recall of pantoprazole-containing products was associated with a higher rate of pDDIs. However, the prevalence of pDDIs gradually decreased over time. We highlight the importance of planning of recall process and coordinating all potential stakeholders to avoid potential harms.Word count: 1450.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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