Health equity considerations in pragmatic trials in Alzheimer's and dementia disease: Results from a methodological review
Bibliographic record
Abstract
Introduction: To improve dementia care delivery for persons across all backgrounds, it is imperative that health equity is integrated into pragmatic trials. Methods: We reviewed 62 pragmatic trials of people with dementia published 2014 to 2019. We assessed health equity in the objectives; design, conduct, analysis; and reporting using PROGRESS-Plus which stands for Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital, and other factors such as age and disability. Results: Two (3.2%) trials incorporated equity considerations into their objectives; nine (14.5%) engaged with communities; 4 (6.5%) described steps to increase enrollment from equity-relevant groups. Almost all trials (59, 95.2%) assessed baseline balance for at least one PROGRESS-Plus characteristic, but only 10 (16.1%) presented subgroup analyses across such characteristics. Differential recruitment, attrition, implementation, adherence, and applicability across PROGRESS-Plus were seldom discussed. Discussion: Ongoing and future pragmatic trials should more rigorously integrate equity considerations in their design, conduct, and reporting. Highlights: Few pragmatic trials are explicitly designed to inform equity-relevant objectives.Few pragmatic trials take steps to increase enrollment from equity-relevant groups.Disaggregated results across equity-relevant groups are seldom reported.Adherence to existing tools (e.g., IMPACT Best Practices, CONSORT-Equity) is key.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".