Satisfaction with a primary care-based diabetes management program / by Cheryl Harrison-Barnet.
Bibliographic record
Abstract
Diabetes is the sixth leading cause of death in the United States and affects approximately 7% of \nthe population (Hupke, Camp, Chaufoumier, Langley, & Little, 2004; Piatt et al., 2006). It is an \nestablished fact that the long-term complications of diabetes can be reduced by tight glycemic \ncontrol. There is a clear relationship between control of blood glucose, blood pressure, and lipid \nlevel, and the ability to decrease microvascular and macrovascular morbidity (Nutting et al., \n2007). A common measure of blood sugar control is that of glycosylated hemoglobin, or HbAlc. This \nlaboratory test provides a measure of blood sugar control over the previous 3 months (Canadian \nDiabetes Association [CDA], 2007). A Cochrane collaboration review reported that an average \nreduction of HbA1c of 1% or more can result in a 21% reduction in mortality, a 14% reduction in \nacute myocardial infarction (AMI) , and a 37% reduction in microvascular complications if sustained \nover time (as cited in Wagner, Austin, et al., \n2001). \nThere is, however, a gap between this evidence and what is achieved in clinical practice (Nutting \net al., 2007). Wagner, Austin, et al. (2001) argued that fewer than half of patients in the United \nStates with diabetes are receiving proper treatment. A primary care management study of Type 2 \ndiabetes reported that 47.5% of patients had at least one diabetes-related complication (Spann et \nal., 2006). Over half of the patients (60.8%) in this study had a body mass index greater than 30 \nand a mean HbAlc of 7.6%; 35.3% had adequate blood pressure control; and 43.7% had adequate \nlow-density lipoprotein \n(LDL) cholesterol levels.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.138 | 0.020 |
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".