Dietary intake, characteristics, and attitudes of self‐reported low‐carbohydrate dieters
Bibliographic record
Abstract
The study objective was to compare the dietary intakes, characteristics, and attitudes of post‐menopausal women who reportedly followed low‐carbohydrate diets (LCD) to control subjects (CS). Utilizing a cross‐sectional study design, data were obtained by interview, questionnaire, direct measurement, and 24‐hour recall using the multiple‐pass method. Data from 14 women in the LCD group and 15 women in the CS group were used in analysis. Results showed that there were no differences between the groups in age, body mass index, fat free mass, or waist to hip ratio. LCD consumed the same amount of total energy and protein as a percentage of total energy, but consumed significantly more fat as a percentage of total energy (46.0% LCD vs. 33.0% CS, p=0.003) and cholesterol (333 mg LCD vs. 123 mg CS, p=0.007), and significantly less carbohydrate as a percentage of total energy (33.4% LCD vs. 47.4% CS, p=0.003) and fiber (12.1 g LCD vs. 22.3 g CS, p=0.001) than CS. Despite the lower carbohydrate intake in LCD, only 28.6% of these subjects were in ketogenic states. CS scored significantly better than LCD on the Healthy Eating Index (58.2 points LCD vs. 70.4 points CS, p=0.012). More LCD than CS reported engaging in physical activity on a regular basis (59.1% LCD vs. 40.9% CS, p=0.039). CS reported more favorable attitude scores towards foods that contain carbohydrates than LCD (‐2.5 LCD vs. 1.5 CS, p=0.043). In conclusion, LCD do not consume healthy diets and most do not actually consume low‐carbohydrate diets. Research support was provided in part by a the Faculty Grant in Aid program at Arizona State University.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".