Clinical utility of overviews on adverse events of pharmacological interventions
Bibliographic record
Abstract
Abstract Background Overviews (i.e., systematic reviews of systematic reviews, meta-reviews, umbrella reviews) are a relatively new type of evidence synthesis. Among others, one reason to conduct an overview is to investigate adverse events (AEs) associated with a healthcare intervention. Overviews aim to provide easily accessible information for healthcare decision makers including clinicians. We aimed to evaluate the clinical utility of overviews investigating AEs. Methods We used a sample of 27 overviews exclusively investigating drug-related adverse events published until 2021 identified in a prior project. We defined clinical utility as the extent to which overviews are perceived to be useful in clinical practice. Each included overview was assigned to one of seven pharmacological experts with expertise on the topic of the overview. The clinical utility and value of these overviews was determined using a self-developed assessment tool. This included four open-ended questions and a ranking of three clinical utility statements completed by clinicians. We calculated frequencies for the ranked clinical utility statements and coded the answers to the open-ended questions using an inductive approach. Results The overall agreement with the provided statements was high. According to the assessments, 67% of the included overviews generated new knowledge. In 93% of the assessments, the overviews were found to add value to the existing literature. The overviews were rated as more useful than the individual included systematic reviews (SRs) in 85% of the assessments. The answers to the open-ended questions revealed two key aspects of clinical utility in the included overviews. Firstly, it was considered useful that they provide a summary of available evidence (e.g., along with additional assessments, or across different populations or in different settings that have not been evaluated together in the included SRs). Secondly, it was found useful if overviews conducted a new meta-analysis to answer specific research questions that had not been answered previously. Conclusions Overviews on drug-related AEs are considered valuable for clinical practice by clinicians. They can make available evidence on AEs more accessible and provide a comprehensive view of available evidence. As the role of overviews evolves, investigations such as this can identify areas of value.
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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 | 0.258 | 0.482 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".