Proceedings of the Canadian Frailty Network Summit: Medication Optimization for Frail Older Canadians, Toronto, Monday April 24, 2017
Bibliographic record
Abstract
Appropriate and optimal use of medication and polypharmacy are especially relevant to the care of older Canadians living with frailty, often impacting their health outcomes and quality of life. A majority (two thirds) of older adults (65 or older) are prescribed five or more drug classes and over one-quarter are prescribed 10 or more drugs. The risk of adverse drug-induced events is even greater for those aged 85 or older where 40% are estimated to take drugs from 10 or more drug classes. The Canadian Frailty Network (CFN), a pan-Canadian non-for-profit organization funded by the Government of Canada through the Networks of Centres of Excellence Program (NCE), is dedicated to improving the care of older Canadian living with frailty and, as part of its mandate, convened a meeting of stakeholders from across Canada to seek their perspectives on appropriate medication prescription. The CFN Medication Optimization Summit identified priorities to help inform the design of future research and knowledge mobilization efforts to facilitate optimal medication prescribing in older adults living with frailty. The priorities were developed and selected through a modified Delphi process commencing before and concluding during the summit. Herein we describe the overall approach/process to the summit, a summary of all the presentations and discussions, and the top ten priorities selected by the participants.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".