O3‐01‐01: Examining the discontinuation of cholinesterase inhibitor therapy from the perspectives of Alzheimer's disease caregivers and physicians
Bibliographic record
Abstract
Cholinesterase inhibitors (ChEIs) are now widely prescribed first-line drugs in the treatment of mild to moderate Alzheimer's disease and related dementias (ADRD). Yet, there is inconsistent evidence about the benefits of these drugs and how best to determine their effectiveness, particularly when these drugs are prescribed over the long term. The issue of when to discontinue ChEI drugs is thus complex and subject to much debate. Against this backdrop, the experiences of prescribing physicians and family caregivers have been largely ignored even though it has been recognized that discontinuation of this drug treatment may have significant implications for quality of life. This study presents qualitative data from 26 in-depth interviews with caregivers to persons with ADRD who were withdrawn from drug therapy or were in the process of being withdrawn. Additionally, 19 family physicians participated in four focus groups about their prescribing practices. The findings reveal that the decision to discontinue ChEIs involves a complex interplay between caregiver appraisal of the benefits of ChEIs and physician interpretation of ambiguous psychometric findings in the assessment of treated patients. Once treatment began, physicians took their lead from caregivers as to how long to continue prescribing a ChEI even in instances where prescribing might have been contraindicated on the basis of continued poor performance on psychometric testing. Caregivers offered rich narratives about discontinuation being a source of anxiety and conflict within the family, with siblings often disagreeing on the benefits of ChEIs. Caregivers tended to view ChEIs as the last line of defense against the loss of self and felt obliged to continue with treatment despite uncertainty about its effectiveness. The decision of the physician to stop prescribing a ChEI almost always occurred in instances of severe adverse reactions or when institutionalization could no longer be delayed. The study's findings suggest a need for developing “best practice” guidelines to assist physicians and other health care professionals in supporting caregivers and care recipients with the process of ChEI discontinuation. Such initiatives could potentially reduce the costs of funding these drugs and improve the quality of life of caregivers and care recipients.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".