Assessing outcomes of enhanced chronic disease care through patient education and a value-based formulary study (ACCESS)—study protocol for a 2×2 factorial randomized trial
Bibliographic record
Abstract
BACKGROUND: Chronic diseases result in significant morbidity and costs. Although medications and lifestyle changes are effective for improving outcomes in chronic diseases, many patients do not receive these treatments, in part because of financial barriers, patient and provider-level knowledge gaps, and low patient motivation. The Assessing outcomes of enhanced chronic disease care through patient education and a value-based formulary study (ACCESS) will determine the impact of two interventions: (1) a value-based formulary which eliminates copayment for high-value preventive medications; and (2) a comprehensive self-management support program aimed at promoting health behavior change and medication adherence, combined with relay of information on medication use to healthcare providers, on cardiovascular events and/or mortality in low-income seniors with elevated cardiovascular risk. METHODS: The ACCESS study will use a parallel, open label, factorial randomized trial design, with blinded endpoint evaluation in 4714 participants who are over age >65 (and therefore have drug insurance provided by Alberta Blue Cross with 30 % co-payment); are at a high risk for cardiovascular events based on a history of any one of the following: coronary heart disease, prior stroke, chronic kidney disease, heart failure, or any two of the following: current cigarette smoking, diabetes mellitus, hypertension, or hypercholesterolemia; and have a household income
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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 | 0.039 | 0.034 |
| Meta-epidemiology (narrow) | 0.011 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.059 | 0.013 |
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".