Potential for Detection of Safety Signals for Over-the-Counter Medicines Using National ADR Spontaneous Reporting Data: The Example of OTC NSAID-Associated Gastrointestinal Bleeding
Bibliographic record
Abstract
One post-marketing surveillance challenge for many regulatory authorities is access to information regarding the safety of over-the-counter (OTC) medicines. National spontaneous adverse drug reaction (ADR) report data represent a rich potential data source for the detection of safety signals associated with OTC medicines, yet little is known regarding the possibility of detecting safety signals for OTC medicines within these datasets. The aim of this study was to evaluate the potential for detecting safety signals for OTC medicines in National ADR spontaneous reporting data, using OTC non-steroidal anti-inflammatory drugs (NSAIDs) and gastrointestinal bleeding as an example. Data from the Australian Adverse Drug Reactions System (ADRS) dataset (1971-2008) and the Canadian Vigilance Adverse Reaction Online Database (VAROD) (1965-2013) were used to explore the feasibility of using spontaneous reporting data, exploring the association between gastrointestinal bleeding and the use of OTC NSAIDs. Safety signals were examined using disproportionality analyses and reporting odds ratios calculated. After adjusting for age, gender, medications known to increase the risk of bleeding, and medications used for the management of conditions associated with an increased risk of bleeding, a two-fold increase in the risk of gastrointestinal (GI) bleeding with OTC NSAID was observed within each dataset. This study demonstrates that spontaneous ADR reporting data can be used in pharmacovigilance to monitor the safety of OTC medicines.
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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 | 0.099 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".