TRANSLATING KNOWLEDGE ABOUT DEPRESCRIBING INTO PRACTICE TO OPTIMIZE MEDICATION USE IN OLDER ADULTS
Bibliographic record
Abstract
Medications play a significant role in the management of chronic medical conditions in older adults. Polypharmacy (concurrent use of multiple medications) can be appropriate and highly beneficial to the individual. The decision to initiate a medication involves determining the necessity of the medication, then weighing up the potential benefits and potential risks of the medication for the individual. However, the necessity, benefits and risks of medication use in an individual may change with time and the ageing process. Therefore, to achieve quality use of medications in older adults “deprescribing” may be required. Deprescribing is the process of withdrawal (or dose reduction) of medications that are no longer necessary, are high risk, or do not fit with the preferences and treatment goals of the individual (inappropriate medications). Evidence internationally shows that approximately half of all older adults are taking a medication which is potentially inappropriate and, therefore, deprescribing is not occurring in practice as often as it should be. Research is being conducted internationally to highlight the prevalence and associated harms of inappropriate medication use as well as determining the potential benefits and harms of deprescribing. It is imperative that knowledge gained from this research is translated into practice. We have a worldwide ageing population and use of medications in this population is unavoidable. This symposium will present international research and knowledge translation activities that are leading the way to a clinical practice where medications are prescribed and deprescribed judiciously.
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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 | 0.033 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".