Clinical Outcomes of Rural Patients with Diabetes Treated by ECHO-Trained Providers Versus an Academic Medical Center
Bibliographic record
Abstract
BACKGROUND: Despite clinical practice guidelines prioritizing cardiorenal risk reduction, national trends in diabetes outcomes, particularly in rural communities, do not mirror the benefits seen in clinical trials with emerging therapeutics and technologies. OBJECTIVE: Project ECHO supports implementation of guidelines in under-resourced areas through virtual communities of practice, sharing of best practices, and case-based learning. We hypothesized that diabetes outcomes of patients treated by ECHO-trained primary care providers (PCPs) would be similar to those of patients treated by specialists at an academic medical center. DESIGN: Specialists from the University of New Mexico (UNM) launched a weekly diabetes ECHO program to mentor dyads consisting of a PCP and community health worker at ten rural clinics. PARTICIPANTS: We compared cardiorenal risk factor changes in patients with diabetes treated by ECHO-trained dyads to patients treated by specialists at the UNM Diabetes Comprehensive Care Center (DCCC). Eligible participants included adults with type 1 diabetes, type 2 diabetes on insulin, or diabetes of either type with A1c > 9%. MAIN MEASURES: The primary outcome was change from baseline in A1c in the ECHO and DCCC cohorts. Secondary outcomes included changes in body mass index (BMI), blood pressure, cholesterol, and urine albumin to creatinine ratio (UACR). KEY RESULTS: ; p = 0.003 for difference in difference). Diastolic blood pressure declined in the Endo ECHO cohort only. Improvements of similar magnitude were observed in low-density lipoprotein cholesterol in both groups. UACR remained stable in both groups. CONCLUSIONS: ECHO may be a suitable intervention for improving diabetes outcomes in rural, under-resourced communities with limited access to a specialist.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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; 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".