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Record W4311672278 · doi:10.1159/000528083

European Association for the Study of Obesity Position Statement on Medical Nutrition Therapy for the Management of Overweight and Obesity in Adults Developed in Collaboration with the European Federation of the Associations of Dietitians

2022· review· en· W4311672278 on OpenAlexafffund
Maria Hassapidou, Antonis Vlassopoulos, Marianna Kalliostra, Elisabeth Govers, Hilda Mulrooney, Louisa Ells, Ximena Ramos Salas, Giovanna Muscogiuri, Teodora Handjieva Darleska, Luca Busetto, Volkan Yumuk, Dror Dicker, Jason C. G. Halford, Euan Woodward, Pauline Douglas, Jennifer Brown, Tamara Brown

Bibliographic record

VenueObesity Facts · 2022
Typereview
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsOttawa HospitalCanadian Obesity NetworkUniversity of Alberta
FundersMcMaster University
KeywordsMedicineObesityOverweightPsychological interventionMedical nutrition therapyWeight managementMediterranean dietWeight lossGerontologyEnvironmental healthPhysical therapyIntensive care medicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Obesity affects nearly 1 in 4 European adults increasing their risk for mortality and physical and psychological morbidity. Obesity is a chronic relapsing disease characterized by abnormal or excessive adiposity with risks to health. Medical nutrition therapy based on the latest scientific evidence should be offered to all Europeans living with obesity as part of obesity treatment interventions. METHODS: A systematic review was conducted to identify the latest evidence published in the November 2018-March 2021 period and to synthesize them in the European guidelines for medical nutrition therapy in adult obesity. RESULTS: Medical nutrition therapy should be administered by trained dietitians as part of a multidisciplinary team and should aim to achieve positive health outcomes, not solely weight changes. A diverse range of nutrition interventions are shown to be effective in the treatment of obesity and its comorbidities, and dietitians should consider all options and deliver personalized interventions. Although caloric restriction-based interventions are effective in promoting weight reduction, long-term adherence to behavioural changes may be better supported via alternative interventions based on eating patterns, food quality, and mindfulness. The Mediterranean diet, vegetarian diets, the Dietary Approaches to Stop Hypertension, portfolio diet, Nordic, and low-carbohydrate diets have all been associated with improvement in metabolic health with or without changes in body weight. In the November 2018-March 2021 period, the latest evidence published focused around intermittent fasting and meal replacements as obesity treatment options. Although the role of meal replacements is further strengthened by the new evidence, for intermittent fasting no evidence of significant advantage over and above continuous energy restriction was found. Pulses, fruit and vegetables, nuts, whole grains, and dairy foods are also important elements in the medical nutrition therapy of adult obesity. DISCUSSION: Any nutrition intervention should be based on a detailed nutritional assessment including an assessment of personal values, preferences, and social determinants of eating habits. Dietitians are expected to design interventions that are flexible and person centred. Approaches that avoid caloric restriction or detailed eating plans (non-dieting approaches) are also recommended for improvement of quality of life and body image perceptions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.044
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.087
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0060.010
Science and technology studies0.0020.002
Scholarly communication0.0060.003
Open science0.0030.005
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0170.007

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 head0.041
GPT teacher head0.338
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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

Citations110
Published2022
Admission routes2
Has abstractyes

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