A systematic review of interventions to reduce anticholinergic burden in older people with dementia in primary care
Bibliographic record
Abstract
OBJECTIVE: This systematic review aimed to assess the types and effectiveness of interventions that sought to reduce anticholinergic burden (ACB) in people with dementia (PwD) in primary care. METHODS: One trial registry and eight electronic databases were systematically searched to identify eligible English language studies from inception until December 2021. To be eligible for inclusion, studies had to be randomised controlled trials (RCTs) or non-randomised studies (NRS), including controlled before-and-after studies and interrupted time-series studies, of interventions to reduce ACB in PwD aged ≥65 years (either community-dwelling or care home residents). All outcomes were to be considered. Quality was to be assessed using the Cochrane Risk of Bias tool for RCTs and ROBINS-I tool for NRS. If data could not be pooled for meta-analysis, a narrative synthesis was to be conducted. RESULTS: In total, 1880 records were found, with 1594 records remaining after removal of duplicates. Following title/abstract screening, 13 full-text articles were assessed for eligibility. None of these studies met the inclusion criteria for this review. Reasons for exclusion were incorrect study design, ineligible study population, lack of focus on reducing ACB, and studies conducted outside the primary care setting. CONCLUSIONS: This 'empty' systematic review highlights the lack of interventions to reduce ACB in PwD within primary care, despite this being highlighted as a priority area for research in recent clinical guidance. Future research should focus on development and testing of interventions to reduce ACB in this patient population through high-quality clinical trials.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".