Digging deep into diabetes: achieving better glycemic control in diabetic patients in a resident-run clinic
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) affects over 30 million Americans with an estimated annual cost of $327 billion in 2017. Patients with diabetes, especially with financial and/or social hardships, pose challenges in achieving target hemoglobin A1c (HbA1c) values. Understanding patient-specific barriers offer opportunities to improve outcomes in patient care.Objective: We aimed to improve a patient’s glycemic control by reducing barriers to care. Furthermore, we evaluated the impact that a resident quality improvement effort had on providing high value diabetic care.Methods: We performed a retrospective cohort study of patients with HbA1c >9.0% in an underserved, resident-run clinic. Patients were surveyed on their knowledge of diabetes and reported obstacles to achieve diabetic control. We then implemented a 12 -month customized, patient-directed, multi-modal, multidisciplinary intervention.Results: Ninety-four patients with HbA1c >9.0% were identified, 65 surveyed, and 51 included in the intervention phase. After the intervention phase, re-evaluation of HbA1c in a paired sample comparison showed that the average HbA1c had decreased by 1.41% (11.28% vs. 9.87%, p < 0.01). Among the patients included in the intervention group, approximately 8% had their HbA1c reduced by ≥50% from their baseline, 23% had their HbA1c reduced by ≥25% from their baseline and 49% had their HbA1c reduced by ≥10% from their baseline.Conclusions: A strategically designed, a patient-centered customized intervention can have a positive impact on a patient’s diabetic control.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".