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 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 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".