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 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 | 0.453 | 0.698 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".