004 Co-designing a consult patient decision aid for deprescribing cholinesterase inhibitors
Bibliographic record
Abstract
Introduction People living with dementia take many medications, some of which may become high-risk, unnecessary, or ineffective as dementia progresses. Up to one third of cholinesterase inhibitors (ChEIs) are continued for longer than appropriate. Deprescribing (reducing or stopping) these medications remains limited. Our aim was to co-design a consult patient decision aid (CPtDA) to support shared decision making between healthcare professionals and consumers to make decisions about deprescribing ChEIs. Methods A systematic process using the International Patient Decision Aids Standards to develop Patient Decision Aids was employed. Development involved assembling a steering group and defining the CPtDA’s purpose, scope, and target audience. Interviews with consumers and healthcare professionals were conducted to gain feedback on the content, format, structure, comprehensibility, and usability. Results A steering group composed of healthcare professional and consumer representatives was assembled. The group developed the draft prototype so that it was suitable for further testing. Interviews were conducted with 6 healthcare professionals and 11 consumers. Iterative changes to improve the content, format and structure of the decision aid were made over three rounds of modifications. The main changes included rewording the purpose of the decision aid and simplifying its layout and format. Participants reported that the decision aid is comprehensible and may be useful in clinical practice. Discussion Limited co-designed resources exist to guide shared decision making about deprescribing decisions for people living with dementia. Our co-designed CPtDA could help people living with dementia and their carers to consider their goals of care and decide to continue or deprescribe their ChEI alongside their healthcare professional. Conclusion(s) Using the CPtDA in practice will support shared decision making about the continued need of ChEIs. This may lead to increased deprescribing, better aligning medication use with patient goals.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".