Criteria to Report Adverse Drug Withdrawal Events in Clinical Trials: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Polypharmacy is a major risk factor for adverse drug events (ADEs), which are a common cause of hospitalization, especially among older adults. Deprescribing is a promising strategy to prevent ADEs; however, clinicians may hesitate to deprescribe for fear of causing adverse drug withdrawal events (ADWEs). Collectively, ADWEs are the re-emergence of symptoms or a disease state due to the discontinuation of a medication. Although capturing ADWEs is critical to understanding the complications that might arise from deprescribing, these events may not be routinely or systematically captured in clinical trials. OBJECTIVES: We aimed to determine the frequency of ADWE reporting, compare the strengths and limitations of different approaches, and compare the rates of the number of ADWEs detected across trials. METHODS: A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. The search strategy was developed with a research librarian, and studies were identified using Ovid Medline, Embase, and the Cochrane Central Register of Controlled Trials from inception to July 2, 2024. We included all randomized controlled trials testing a deprescribing intervention in older adults (mean or median age ≥ 65 years) and analyzed a subsample of the studies reporting ADWEs as an outcome. RESULTS: Among the 139 eligible studies that were identified, only 12 reported an ADWE. These studies utilized 6 approaches to capture ADWEs: Naranjo ADWE Probability Scale; clinical monitoring for specific withdrawal symptoms; identification through ICD-10 codes; identification of ADWEs as a subset of confirmed ADEs; patient/caregiver self-report; and clinical judgment. CONCLUSION: Results confirmed that few deprescribing studies capture ADWEs and there is a lack of standardized reporting. A harmonized approach to capturing ADWEs with specific criteria could ensure more consistent results in deprescribing trials, improve our understanding of this important outcome, and facilitate future meta-analyses.
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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 | 0.122 | 0.247 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.036 | 0.028 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".