Causality assessment of adverse events by healthcare professionals in an academic hospital setting: a descriptive retrospective study
Bibliographic record
Abstract
Assessing the causality of a drug product-related adverse event (AE) is a very important aspect of pharmacovigilance. However, it is unclear whether AEs are investigated for causality in a hospital setting. The aims of this study were to (1) evaluate the proportion of AEs for which causality is sought and (2) list the causality assessment (CA) tools used by healthcare professionals. This retrospective study includes 500 randomized patients (125 patients/year) admitted to a tertiary care academic hospital between 2018 and 2021. Electronic medical records were reviewed and relevant variables were extracted: (1) demographic, (2) hospitalization, (3) drug product, (4) AE, and (5) CA. A descriptive analysis was carried out (median, minimum–maximum, proportion) to characterize our sample. The characteristic of our sample was as follows: median age 69 years old (range: 21–96 years), 43.6% female, median comorbidities/patient 4 (0–12), and median hospital stay of 3 days (1–19). We identified a total of 9568 drug products and 2541 AEs, among these, 302 (8.4%) were serious AEs. No CA ( n = 0) or CA tools ( n = 0) were found in our sample. In this study, we report that no AEs, whether serious or non-serious, were subject to documented CA. Key points: Adverse events among drug users are frequent during hospital stay. Causality assessment of adverse events was poorly documented by healthcare professionals in a hospital setting.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".