Diabetic and Obese Patient Clinical Outcomes Improve During a Care Management Implementation in Primary Care
Bibliographic record
Abstract
BACKGROUND: To address the increasing burden of chronic disease, many primary care practices are turning to care management and the hiring of care managers to help patients coordinate their care and self-manage their conditions. Care management is often, but not always, proving effective at improving patient outcomes, but more evidence is needed. METHODS: In this pair-matched cluster randomized trial, 5 practices implemented care management and were compared with 5 comparison practices within the same practice organization. Targeted patients included diabetic patients with a hemoglobin A1c >9% and nondiabetic obese patients. Clinical values tracked were A1c, blood pressure, low-density lipoprotein, microalbumin, and weight. RESULTS: Clinically important improvements were demonstrated in the intervention versus comparison practices, with diabetic patients improving A1c control and obese patients experiencing weight loss. There was a 12% relative increase in the proportion of patients meeting the clinical target of A1c <7% (95% CI, 3%-20%), and 26% of obese nondiabetic patients in chronic care management practices lost 5% or more of their body weight as compared with 10% of comparison patients (adjusted relative improvement, 15%; CI, 2%-28%). CONCLUSIONS: These findings add to the growing evidence-base for the effectiveness of care management as an effective clinical practice with regard to improving diabetes- and obesity-related outcomes.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".