Comparative Effectiveness Research in DARTNet Primary Care Practices
Bibliographic record
Abstract
BACKGROUND: The Distributed Ambulatory Research in Therapeutics Network (DARTNet) is a federated network of electronic health record (EHR) data, designed as a platform for next-generation comparative effectiveness research in real-world settings. DARTNet links information from nonintegrated primary care clinics that use EHRs to deliver ambulatory care to overcome limitations with traditional observational research. OBJECTIVE: Test the ability to conduct a remote, electronic point of care study in DARTNet practices by prompting clinic staff to obtain specific information during a patient encounter. RESEARCH DESIGN: Prospective survey of patients identified through queries of clinical data repositories in federated network organizations. On patient visit, survey is triggered and data are relinked to the EHR, de-identified, and copied for evaluation. SUBJECTS: Adult patients diagnosed with diabetes mellitus that scheduled a clinic visit for any reason in a 2-week period in DARTNet primary care practices. MEASURES: Survey on hypoglycemic events (past month) and over-the-counter and herbal supplement use. RESULTS: DARTNet facilitated point of care data collection triggered by an electronic prompt for additional information at a patient visit. More than one-third of respondents (33% response rate) reported either mild (45%) or severe hypoglycemic events (5%) in the month before the survey; only 3 of those were also coded using the ICD-9 (a significant difference in detection rates 37% vs. 1%). Nearly one-quarter of patients reported taking an OTC/herbal, 4% specifically for the treatment of symptoms of diabetes. CONCLUSIONS: Prospective data collection is feasible in DARTNet and can enable comparative effectiveness and safety research.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".