MétaCan
Menu
Back to cohort
Record W3152315752 · doi:10.4103/cjrm.cjrm_1_20

New obesity treatment: Fasting, exercise and low carb diet - The NOT-FED study

2021· article· en· W3152315752 on OpenAlexaffvenueabout
Len Kelly, Terry O′Driscoll, Robert Minty, Denise Poirier, Jenna Poirier, Wilma Hopman, Hannah Willms, Aidan Goertzen, Sharen Madden

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsQueen's UniversityUniversity of WaterlooNexen (Canada)NOSM University
Fundersnot available
KeywordsWaistMedicineWeight lossObesityBody mass indexObservational studyBlood pressureGerontologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.282
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Rural MedicineSame topicDietary Effects on HealthFrench-language works237,207