Adverse Drug Events in Older Adults: Review of Adjudication Methods in Deprescribing Studies
Bibliographic record
Abstract
OBJECTIVES: Polypharmacy is common in older adults and associated with adverse drug events (ADEs). Several methods have been described in studies to help correlate ADE causation. We performed a narrative review to identify methods for ADE adjudication. We compared their strengths and limitations to assess their applicability to deprescribing studies (of which clinical trials are a subset) and to encourage the use of a standardized method in future studies. DESIGN: We performed a review of original articles (1946-2019) using the Medline (Ovid) and Cochrane databases. We also conducted a manual reference search of review articles. Abstracts were screened for relevance. MEASUREMENTS: Adjudication methods were compared for advantages and limitations including validity, ease of use, and applicability to clinical trials with deprescribing as the primary intervention. RESULTS: The search yielded 1881 articles of which 175 articles were included for full-text review. Following in-depth review, 135 were excluded: 79 had no ADE outcome data, 35 were not specific to older adults, 9 were not relevant, 6 were review articles, 5 contained duplicate data, and 1 was not written in French or English. Forty articles remained for analysis, from which we identified 10 unique ADE adjudication methods. No method was developed originally for use in a deprescribing setting. CONCLUSION: A standard method to identify ADEs is important to capture the outcome reliably in deprescribing studies. All methods we identified had limitations in terms of capturing adverse events from the withdrawal of medications. Future work should focus on refining adjudication methods for capturing ADEs related not only to medication continuation and new drug starts but also to deprescribing and drug discontinuation. J Am Geriatr Soc 68:1594-1602, 2020.
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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 | 0.115 | 0.323 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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; 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".