Is aggregate use of published case reports as a viable option for augmenting pharmacovigilance? An applied example of pembrolizumab-related adverse events in patients with non-small cell lung cancer receiving first-line treatment.
Bibliographic record
Abstract
e18840 Background: Pharmacovigilance suffers from considerable underreporting, and causality is rarely confirmed. We aim to examine the validity of the aggregate use of published case reports as a viable option for augmenting pharmacovigilance. We explore the setting of first line pembrolizumab treatment for non-small cell lung cancer as safety signals in this cohort are relatively well documented and because drug-related adverse events are more easily confirmed with no previous drug treatments. Methods: We compared immune-related adverse event rates (Any and Severe) associated with pembrolizumab from three select first non-small lung cancer KEYNOTE clinical trials (024, 042, and 189) to clinician confirmed pembrolizumab-related adverse event rates from a synthetic cohort case report data from a matching population. The latter were retrieved from the OpenCaseTM database and included 126 non-small cell lung cancer patients, of which 110 had received pembrolizumab monotherapy and 16 pembrolizumab in combination with a platinum-based chemotherapy regimen. Baseline characteristics with respect to age, sex, smoking history, non-drug treatment history, PD-L1 tumor proportion score, and key genetic markers (e.g. EGFR, ALK) were acceptably similar (results will be presented in a table). Results: In the synthetic cohort of case reports, Immune-related adverse events, which were clinician confirmed to be related to pembrolizumab, occurred in 86/126 (68.2%). Life threatening and severe treatment-related AEs were 7.4% and 65% respectively. These were substantially higher than reported in clinical trials 8.0%-9.7% experienced severe immune-related events. The majority of the most frequently occurring immune-related AEs in the KEYNOTE trials had similar reported proportions to the case reports. For example, hypothyroidism (any grade) ranged from 6.7% to 9.1% in the KEYNOTE trials compared to 4.9% among the case reports, and pneumonitis (any grade) ranged from 4.4% to 12.0% in the trials compared to 5.5% in the case reports. Other events such as myositis, type 1 diabetes, liver dysfunction, kidney injury, and colitis were frequent in the cohort of case reports (3.1%-7.9%), but rare or unreported in all KEYNOTE trials (0.0%-2.2%). Conclusions: Overall, adverse event rates in the synthetic cohort of case reports were either similar or higher than observed in clinical trials of matching populations. This observation provides rationale for utilizing case reports in pharmacovigilance practice, specifically for rarer events.
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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 | 0.355 | 0.653 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".