Treated glycosylated hemoglobin levels in individuals with diabetes mellitus vary little by health status
Bibliographic record
Abstract
As choosing wisely has raised the issue of whether some individuals with type 2 diabetes may be overtreated, we examined the intensity of glycemic control across health status strata defined by comorbidities or frailty.This is a retrospective cohort study of commercially insured patients from 50 US states (Clinformatics Data Mart). We evaluated treated HbA1c levels in adults with new diabetes diagnosed between January 2004 and December 2009 who had HbA1C measured after at least 1 year of follow-up.Of 191,590 individuals with diabetes, 78.5% were otherwise healthy, 10.6% had complex health status (3 or more chronic conditions), and 10.9% were very complex (Johns Hopkins Adjusted Clinical Groups frailty marker or end-stage chronic disease). The proportion of patients who were tightly controlled (HbA1C <7%) was similar in otherwise healthy patients (66.1%) and in complex patients (65.8%, P = 0.37), and although it was lower (60.9%, P < 0.0001) in very complex patients, the magnitude of the difference was small. A substantial proportion of complex/very complex patients were taking sulfonylurea or insulin despite being at an increased risk for adverse effects from these agents and having tightly controlled HbA1C: 40.6% had HbA1C <7% and 24% had HbA1C <6.5%. Among patients with HbA1C <7%, use of insulin or sulfonylureas was associated with an increased risk for all-cause hospitalization [aHR 1.54, 95% confidence interval (95% CI) 1.45-1.64] and for emergency room visits (aHR 1.44, 95% CI 1.35-1.53) over the subsequent median 6 months follow-up.Diabetic control was similar regardless of comorbidity burden and frailty status. Despite being at a higher risk for adverse effects, nearly half of complex and very complex patients were still receiving insulin or sulfonylureas despite having treated HbA1C levels <7%, and these patients did exhibit higher risk of all-cause hospitalizations or emergency visits subsequently.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".