A Practice-Based Research Network Focused on Comparative Effectiveness Research in Type 2 Diabetes Management
Bibliographic record
Abstract
PURPOSE: To establish a real-world research platform focused on comparative effectiveness research and health care decision making in diabetes care in order to obtain a detailed understanding of individualized patient management in primary care. METHODS: Diabetes FORWARD (Foundation of Real-World Assessment and Research in Diabetes) is a North American research platform being organized to conduct longitudinal, noninterventional investigations of an anticipated 10,000 patients with type 2 diabetes mellitus (T2DM). Recruitment will be stratified to reflect typical (primarily primary care) clinical T2DM populations. Streamlined data collection relying on electronic medical records (retrospective) and periodic surveys (prospective) will reduce the burden of study participation and, therefore, enhance enrollment by busy primary care and endocrinology practices. Physician data will include baseline demographic and practice information. Patient data will include demographics, T2DM characteristics and treatment, resource utilization information, and patient-reported outcomes. Responses can be tracked within the observation window in near-real time, allowing immediate, noninterventional reaction at the point of nonresponse. EXPECTED OUTCOMES: Diabetes FORWARD is expected to provide important real-world data describing how actual clinical T2DM management differs across sites, settings, and clinicians, and its impact on glycemic control, treatment adherence and persistence, and clinical outcomes. These data will also help to identify the effect of diabetes management on the onset and progression of retinopathy, neuropathy, nephropathy, and cardiovascular disease at 6-month intervals. CONCLUSION: To our knowledge, Diabetes FORWARD is the first diabetes-focused, practice-based research network in the United States and Canada. The current study will provide robust data that should reflect typical management of T2DM in clinical practice in North America.
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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 | 0.427 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".