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Health Benefits of Long-Term Weight-Loss Maintenance

2015· review· en· W2159352552 on OpenAlexaff
Christian F. Rueda‐Clausen, Ayodele A. Ogunleye, Arya M. Sharma

Bibliographic record

VenueAnnual Review of Nutrition · 2015
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWeight lossMedicineObesityQuality of life (healthcare)Intensive care medicinePsychological interventionComorbidityGerontologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Obesity is a chronic and complex medical condition associated with a large number of complications affecting most organs and systems through multiple pathways. Strategies for weight management include behavioral, pharmacological, and surgical interventions, all of which can result in a reduction in obesity-related comorbidities and improvements in quality of life. However, subsequent weight regain often reduces the durability of these improvements. The objective of this article is to review evidence supporting the long-term effects of intentional weight loss on morbidity, mortality, quality of life, and health-care cost. Overall, considerable evidence suggests that intentional weight loss is associated with clinically relevant benefits for the majority of obesity-related comorbidities. However, the degree of weight loss that must be achieved and sustained to reap these benefits varies widely between comorbidities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.376
Teacher spread0.324 · 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.

Study designSystematic review
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

Citations89
Published2015
Admission routes1
Has abstractyes

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