Changes in anticholinergic load from regular prescribed medications in palliative care as death approaches
Bibliographic record
Abstract
Although there is an understandable emphasis on the side effects of individual medications, the cumulative effects of medications have received little attention in palliative care prescribing. Anticholinergic load reflects a cumulative effect of medications that may account for several symptoms and adverse health outcomes frequently encountered in palliative care. A secondary analysis of 304 participants in a randomised controlled trial had their cholinergic load calculated using the Clinician-Rated Anticholinergic Scale (modified version) longitudinally as death approached from medication data collected prospectively by study nurses on each visit. Mean time from referral to death was 107 days, and mean 4.8 visits were conducted in which data were collected. Relationships to key factors were explored. Data showed that anticholinergic load rose as death approached because of increasing use of medications for symptom control. Symptoms significantly associated with increasing anticholinergic load included dry mouth and difficulty concentrating (P < 0.05). There were also significant associations with increasing anticholinergic load and decreasing functional status (Australia-modified Karnofsky Performance Scale; and quality of life (P < 0.05). This study has documented in detail the longitudinal anticholinergic load associated with medications used in a palliative care population between referral and death, demonstrating the biggest contributor to anticholinergic load in a palliative care population is from symptom-specific medications, which increased as death approached.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".