Active‐comparator restricted disproportionality analysis for pharmacovigilance signal detection studies of chronic disease medications: An example using sodium/glucose cotransporter 2 inhibitors
Bibliographic record
Abstract
AIMS: Disproportionality analysis is a common pharmacovigilance tool to detect safety signals of type 2 diabetes medications from spontaneous drug reporting databases. The aim was to demonstrate the impact of using active-comparator restricted disproportionality analysis (ACR-DA), wherein the reference group is restricted to reports with a clinically appropriate active comparator. METHODS: Using reports from the Food and Drug Administration Adverse Event Reporting System, we assessed if sodium/glucose cotransporter 2 (SGLT2) inhibitors are associated with higher reporting of 5 potential adverse events: acute kidney injury, genitourinary tract infections, diabetic ketoacidosis, fractures, and amputations. For each adverse event, we calculated the proportional reporting ratio (PRR) and adjusted reporting odds ratio (aROR [95% confidence interval, CI]) using 3 types of reference groups: no SGLT2 inhibitor (background risk reference), other diabetes drugs (therapeutic class reference), and dipeptidyl peptidase 4 inhibitors (active comparator reference). RESULTS: Based on ACR-DA, we did not detect a safety signal for acute kidney injury (PRR 0.92 [0.81-1.04]; aROR 0.78 [95% CI 0.72-0.85]) or fractures (PRR 0.44[95% CI 0.17-1.15]; aROR 0.74 [95% CI 0.61-0.91]) associated with SGLT2 inhibitors compared to dipeptidyl peptidase 4 inhibitors. However, we detected safety signals for genitourinary tract infections (PRR 2.75[2.02-3.76]; aROR 2.54[2.26-2.86], diabetic ketoacidosis (PRR 63.85[39.37-103.53; aROR 91.49[70.66-118.48]), and amputations (PRR 52.60 [19.66-140.75]; aROR 22.64 [15.32-33.42]. CONCLUSION: The use of the proposed ACR-DA to detect safety signals of type 2 diabetes medications may reduce false positive safety signals through careful selection of the comparator which is expected to reduce channelling bias.
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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 | 0.251 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".