Evaluation of real-world evidence to assess health outcomes related to deprescribing medications in older adults: an International Society for Pharmacoepidemiology–endorsed systematic review of methodology
Bibliographic record
Abstract
Observational studies using real-world data (RWD) can address gaps in knowledge on deprescribing medications but are subject to methodological issues. Limited data exist on the methods employed to use RWD to measure the effects of deprescribing. To describe methodological approaches used in observational studies of deprescribing medications in older adults, we conducted a systematic review in Medline for observational studies published in English (January 1, 2000, to September 14, 2023) that examined the health effects of medication deprescribing in older adults. We described study characteristics and methods, focusing on the operationalization of deprescribing as an exposure and potential time-related biases. Forty-five studies were included, representing a variety of drug classes (eg, statins, aspirin, bisphosphonates) and diseases. Most studies adequately addressed potential time-related biases. The definition of deprescribing was not clearly defined in 12 studies. There was heterogeneity regarding the minimum duration of time that qualified as deprescribing, even within a drug class; fewer than one-third of studies provided a justification for these definitions. Observational studies are common to examine the effects of deprescribing; however, there were inconsistencies in measuring deprescribing and a lack of transparency in reporting. There is a need for minimum sufficient reporting criteria for observational studies on deprescribing.
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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 | 0.339 | 0.586 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".