ANTICHOLINERGIC DRUG USE BEFORE AND AFTER ATTENDING A GERIATRIC DAY HOSPITAL PROGRAM
Bibliographic record
Abstract
Anticholinergic drugs are used frequently by older adults despite their sub-optimal adverse effect characteristics. 126 medication profiles of patients who attended the local Geriatric Day Hospital/Falls Clinic program of the Nova Scotia Health Authority in Halifax, Nova Scotia, Canada between January and June 2018 were reviewed. Comparisons were made in the key areas of total number of medications and anticholinergic drug burden using 2 risk scores - Anticholinergic Cognitive Burden (ACB) and Drug Burden Index (DBI) - at the time of admission to the program and at completion. The mean number of medications before the program was 9.84 versus 9.50 at completion. Both ACB and DBI scores showed improvement at program completion as compared to admission, with the mean ACB score falling from 1.44 to 1.32, and the mean DBI score decreasing from 0.63 to 0.62. Although these changes were modest, this review demonstrates the importance of thorough medication scrutiny at settings such as geriatric day hospitals, with possible benefits of decreased anticholinergic burden translating into a reduction in medication-associated hazards such as falls, cognitive impairment and functional decline. Qualitative analysis of charts in this study showed that the most common obstacle to acceptance of medication changes by older adults appears to be a lack of motivation to do so and the erroneous belief that they are deriving ongoing benefit from a particular drug that in reality may be more harmful than beneficial. Success in reducing dangerous medications may therefore lie in better education and counseling of older adults.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".