Dietary Intakes in the Nutritional Management Of Gestational Diabetes Mellitus
Bibliographic record
Abstract
PURPOSE: Changes were examined in energy intakes and percentage of energy from macronutrients in response to nutritional intervention in women with gestational diabetes mellitus (GDM). METHODS: The study included 17 women with GDM and 27 women with normal glucose tolerance (controls). Women with GDM were followed by a multidisciplinary team; they received dietary counselling by a registered dietitian, and were prescribed diets with 40% to 45% energy from carbohydrate (CHO), 20% to 25% from protein, and 30% to 35% from fat. Dietary intakes were assessed with food frequency questionnaires before the intervention (26.9 ± 3.8 weeks) and after the intervention (32.6 ± 0.6 weeks). RESULTS: After the intervention, women with GDM reduced their total energy intake to reach lower values than did controls (P value for time-group interaction =0.05). A concomitant reduction in total CHO and glucose intakes in women with GDM led to significantly lower values compared with intakes in controls (P values for time-group interaction =0.001 for all). The post-intervention rate of weight gain in women with GDM was within the Institute of Medicine (IOM)-recommended values, while the post-intervention rate of weight gain in controls was above IOM-recommended values (0.30 ± 0.27 versus 0.61 ± 0.50 kg/week, P≤0.05). CONCLUSIONS: These results suggest that this multidisciplinary medical and nutritional intervention was effective in the achievement of prescribed macronutrient distribution and controlling gestational weight gain in Canadian women with GDM.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".