Medication use quality and safety in older adults: 2022 update
Bibliographic record
Abstract
Improving the quality of medication use and medication safety are important priorities for healthcare providers who care for older adults. The objective of this article was to identify four exemplary articles with this focus in 2022. We selected high-quality studies from an OVID search and hand searching of major high impact journals that advanced the field of research forward. The chosen articles cover domains related to deprescribing, medication safety, and optimizing medication use. The MedSafer Study, a cluster randomized clinical trial in Canada, evaluated whether patient specific deprescribing reports generated by electronic decision support software resulted in reduced adverse drug events in the 30 days post hospital discharge in older adults (domain: deprescribing). The second study, a retrospective cohort study using data from Premier Healthcare Database, examined in-hospital adverse clinical events associated with perioperative gabapentin use among older adults undergoing major surgery (domain: medication safety). The third study used an open-label parallel controlled trial in 39 Australian aged-care facilities to examine the effectiveness of a pharmacist-led intervention to reduce medication-induced deterioration and adverse reactions (domain: optimizing medication use). Lastly, the fourth study engaged experts in a Delphi method process to develop a consensus list of clinically important prescribing cascades that adversely affect older persons' health to aid clinicians to identify, prevent, and manage prescribing cascades (domain: optimizing medication use). Collectively, this review succinctly highlights pertinent topics related to promoting safe use of medications and promotes awareness of optimizing older adults' medication regimens.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".