Dietitian-coached management in combination with annual endocrinologist follow up improves global metabolic and cardiovascular health in diabetic participants after 24 months
Bibliographic record
Abstract
This 24 month study evaluated the effect of dietitian coaching combined with minimal endocrinologist follow up on the glycemic control and cardiovascular risks of diabetic participants, compared with conventional endocrinologist follow up. Participants with type 1 or type 2 diabetes were assigned to either the control group with conventional endocrinologist follow up (C; n = 50) or the dietitian-coached group (DC; n = 51) with on-site diabetes self-management education every 3 months combined with annual endocrinologist followup. Over the 24 month intervention, weight (-0.7 vs. +2.1 kg; p = 0.04), BMI (+0.3 vs. +0.7 kg/m(2); p = 0.009), and waist circumference (-1.3 vs. +2.4 cm; p = 0.01) significantly differed between the DC and control groups. HbA(1C) dropped significantly in participants of the DC versus the control group (-0.6% vs.-0.3%; p = 0.04). This was accompanied by improved overall energy intake (-548 vs. -74 kcal/day; p = 0.04). However, no link associated glycemic control to nutrient intake or intensiveness of pharmacotherapy. Coaching by a dietitian improves glycemic control and reduces certain cardiovascular risk factors in diabetic subjects, demonstrating that a joint dietitian-endocrinologist model of care provides a convenient strategy for cardiovascular risk management in the diabetic population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".