Practitioners' Use of Clinical Practice Guidelines: An Evidence-Based Approach
Bibliographic record
Abstract
Pre-diabetes is a serious health problem in the United States. Distinguished by plasma glucose levels that are above the normal threshold, patients with pre-diabetes are 10 times more likely to develop type 2 diabetes. Patients with pre-diabetes suffer the same complications as patients with diabetes including diabetic retinopathy, nephropathy, and microalbuminuria. There is considerable evidence to support the idea that early identification and aggressive treatment of pre-diabetes has the potential to delay disease progression. The American Diabetes Association’s clinical practice guideline recommends management of with lifestyle modification and metformin for patients who are at risk for developing type 2 diabetes. The purpose of this project was to evaluate the implementation of the 2012 ADA clinical practice guidelines regarding the management of patients with pre-diabetes by the health care providers at a volunteer-run clinic located in a large metropolitan area in the southeastern United States. This study, even with a small sample size (n=26) revealed that the providers at the clinic had not implemented the 2012 ADA clinical practice guidelines. Clinical practice guidelines promote health care interventions that have proven benefits and improve the consistency of care provided to patients. The greatest benefits of implementing clinical practice guidelines for patients with pre-diabetes are early diagnosis and aggressive disease management. This would improve patient outcomes and in the long run, decrease the cost of medical care.
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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 | 0.307 | 0.511 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".