New obesity treatment: Fasting, exercise and low carb diet - The NOT-FED study
Bibliographic record
Abstract
INTRODUCTION: Due to high rates of obesity in Canada, weight loss is an important primary care challenge. Recent innovations in strategies include intermittent fasting and low-carbohydrate diets, with limited research in a rural setting. METHODS: This prospective 1-year observational study provided patients in Sioux Lookout, Northwestern Ontario with information on fasting and low-carbohydrate diets. Patients were recommended to attend every 3 months for measurements of weight, waist circumference, body mass index (BMI) and blood pressure. Initial and 6-month bloodwork included A1c and Lipids. A survey of health status and diet was administered at 6 months. RESULTS: Of the 94 initial registrants, 36 participants completed 1 year and achieved a 9% weight loss and an 8.6% decrease in BMI and waist circumference. Most participants were female with an average age of 60 years. Clinically insignificant changes in blood pressure and serology were observed. Participants reported few side effects and good compliance with intermittent fasting, averaging 15 h/day, 6 days/week. As in other dietary studies, the dropout rate was high at 62%. CONCLUSION: This low-resource initiative was successful in assisting self-selected patients at a rural primary care clinic to achieve significant weight loss at 1-year. This approach is practical and is fertile ground for ongoing research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".