Representation of patients with dementia in clinical trials of donepezil.
Bibliographic record
Abstract
OBJECTIVES: To evaluate the representation of frail older adults in randomized controlled trials (RCTs), and to assess consequences of under representation by analyzing drug discontinuation rates. METHODS: A cohort of older adults newly dispensed donepezil in Ontario between September 2001 and March 2002 was constructed using administrative data. A systematic review of the literature identified RCTs of donepezil. Patients dispensed donepezil were then compared to clinical trial subjects. Discontinuation rates were examined for patients with and without potential contraindications to this drug. RESULTS: There were 6,424 older adults in the Ontario cohort with new claims for donepezil. Ten RCTs evaluating the use of donepezil were identified (n = 3,423). Between 51% and 78% of the Ontario cohort would have been ineligible for RCT enrollment. Patients dispensed donepezil were older (80.3 vs. 73.7 years, p < 0.001) and more likely to be in long-term care (14.1 vs. 7.1%, p < 0.001) than RCT subjects. Overall, 27.8% of the Ontario cohort discontinued donepezil within seven months of initial prescription. Discontinuation rates were significantly higher for patients with a history of obstructive lung disease, active cardiovascular disease, or Parkinsonism. CONCLUSIONS: Fewer than half of the older adults dispensed donepezil in Ontario would have been eligible to participate in the RCTs that established the efficacy of this drug. Discontinuation rates were higher among patient groups not represented in the trials. Clinicians should carefully assess the potential risks and benefits of such drug therapies for older patients with dementia.
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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 | 0.194 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".