<p>Optimizing glycemic control in type 2 diabetic patients through the use of a low-carbohydrate, high-fat, ketogenic diet: a review of two patients in primary care</p>
Bibliographic record
Abstract
Established guidelines continue to promote carbohydrate-rich (>130 g/day) diets in the primary-care management of type 2 diabetic (DM2) patients. A growing body of evidence suggests that a low-carbohydrate, high-fat, ketogenic diet (KD) may be a more effective nutritional strategy for improving glycemic control. Two diabetic patients, a 65-year-old female and a 52-year-old male, were placed on KDs consisting of 70% fat, 20%-25% protein, and 5%-10% carbohydrates and monitored for 12 weeks. The 65-year-old female demonstrated a 2.4% reduction in HBA1C over 12 weeks while reducing her diabetic medication by 75%. The 52-year-old male demonstrated a 2.5% reduction in HBA1C while eliminating all diabetic medications. These cases demonstrate the efficacy of KDs in terms of improving glycemic control in DM2 patients and lend support to the increased use of KDs in this population cohort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".