Use of Medications With Anticholinergic Effect Predicts Clinical Severity of Delirium Symptoms in Older Medical Inpatients
Bibliographic record
Abstract
BACKGROUND: Use of anticholinergic (ACH) medications is a biologically plausible and potentially modifiable risk factor of delirium, but research findings are conflicting regarding its association with delirium. OBJECTIVES: To evaluate the longitudinal association between use of ACH medications and severity of delirium symptoms and to determine whether this association is modified by the presence of dementia. PATIENTS AND METHODS: A total of 278 medical inpatients 65 years and older with diagnosed incident or prevalent delirium were followed up with repeated assessments using the Delirium Index for up to 3 weeks. Exposure to ACH and other medications was measured daily. The association between change in medication exposure in the 24 hours preceding a Delirium Index assessment was assessed using a mixed linear regression model. RESULTS: During follow-up (mean +/- SD, 12.3 +/- 7.0 days), 47 medications with potential ACH effect were used in the population (mean, 1.4 medications per patient per day). Increase in delirium severity was significantly associated with several measures of ACH medication exposure on the previous day, adjusting for dementia, baseline delirium severity, length of follow-up, and number of non-ACH medications taken. Dementia did not modify the association between ACH medication use and delirium severity. CONCLUSION: Exposure to ACH medications is independently and specifically associated with a subsequent increase in delirium symptom severity in elderly medical inpatients with diagnosed delirium.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".