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Record W2144101638 · doi:10.1503/cmaj.045037

Diet in the management of weight loss

2006· review· en· W2144101638 on OpenAlexafffundvenue
Irène Strychar

Bibliographic record

VenueCanadian Medical Association Journal · 2006
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsCentre Hospitalier de l’Université de MontréalHôpital Notre-Dame
FundersCanadian Institutes of Health ResearchInstitute of Gender and HealthUniversité de Montréal
KeywordsWeight lossOverweightMedicineObesityWeight managementCalorie restrictionPopulationCalorieManagement of obesityAdverse effectRegimenIntensive care medicineEnvironmental healthGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Obesity is an established risk factor for numerous chronic diseases, and successful treatment will have an important impact on medical resources utilization, health care costs, and patient quality of life. With over 60% of our population being overweight, physicians face a major challenge in assisting patients in the process of weight loss and weight-loss maintenance. Low-calorie diets can lower total body weight by an average of 8% in the short term. These diets are well-tolerated and characterize successful strategies in maintaining significant weight loss over a 5-year period. Very-low-calorie diets produce a more rapid weight loss but should only be used for fewer than 16 weeks because of clinical adverse effects. Diets that are severely restricted in carbohydrates (3%-10% of total energy intake) and do not emphasize a reduction of energy intake may be effective in reducing weight in the short term, but there is no evidence that they are sustainable or innocuous in the long term because their high saturated-fat content may be atherogenic. Fat restriction in a weight-loss regimen is beneficial, but the optimal percentage has yet to be determined. Longitudinal trials are needed to resolve these issues. In this article I discuss the evidence for and pitfalls of various types of weight-loss diets and identify issues that physicians need to address in weight loss and weight-loss maintenance.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.826
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.018
GPT teacher head0.298
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations128
Published2006
Admission routes3
Has abstractyes

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