A SIMPLE INTERVENTION TO REDUCE ANTICHOLINERGIC DRUG USE WHILE ATTENDING A GERIATRIC DAY HOSPITAL
Bibliographic record
Abstract
Abstract Attendance at a Geriatric Day Hospital has previously been shown to reduce both the overall number of medications and the number of anticholinergic medications of patients. In the present study, patients enrolled in a Geriatric Day Hospital program from January to February 2019 were divided into a control and intervention group. Anticholinergic medications in the intervention group were flagged by highlighting them in the patient chart and alerting the attending clinician, whereas no alerts were provided in the control group. Anticholinergic load was calculated using the Anticholinergic Cognitive Burden (ACB) and Drug Burden Index (DBI) scores. In comparing admit versus discharge medications in the intervention group, both the mean number of overall medications (10 vs. 9.7) and anticholinergic medications (3.5 vs. 3.1) was reduced; this was not the case in the control group, where the mean number of overall medications remained the same (11.92) and the mean number of anticholinergic medications increased (3.83 vs. 3.92). More significantly, in comparing admit versus discharge scores, both the mean ACB and DBI scores were reduced in the intervention group, but in the control group both the ACB and DBI scores either remained the same or increased at the time of discharge. This clearly shows that a simple intervention (highlighting anticholinergic medications in the patient chart) can have a clinically beneficial outcome of reducing these harmful medications in patients. With approximately 50% of the older population taking at least one anticholinergic drug, the importance of reducing anticholinergic burden cannot be overemphasized.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".