Use of anticholinergic medications predicts symptom severity of delirium in older medical impatients : a prospective cohort study with repeated measurements
Bibliographic record
Abstract
Background. Anticholinergic (ACH) medications are among biologically plausible and potentially modifiable risk factors of delirium. But the epidemiological findings on its role in hospitalized elderly patients are conflicting. Objectives. To evaluate the association between use of ACH medications and delirium severity and the potential effect-modification on this association by dementia. Methods. A cohort of 278 medical inpatients aged 65 years and over with diagnosed delirium was prospectively followed up with the Delirium Index (DI) every 2--7 days up to 3 weeks in a primary acute care hospital. Their DI scores were associated with measures of ACH medication exposure in the previous day using the mixed linear regression model adjusting for potential confounders or effect modifiers. Results . A total of 47 potential ACH medications were used in the cohort (mean: 1.4 per patient per day). An increase in daily ACH medication exposure of one such medication was on average associated with a subsequent increase in delirium severity of 0.52 DI points (95% CI: 0.3--0.8) after adjusting for dementia, baseline DI score, length of follow-up and concurrent use of non-ACH medications. Dementia did not modify the association. Sensitivity analyses using alternative definitions of ACH medications or excluding antipsychotics did not change the results. Conclusions. Exposure to ACH medications is independently and specifically associated with a subsequent increase in symptom severity of delirium among elderly medical inpatients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".