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Record W1855048240 · doi:10.1080/00325481.2014.995067

Extremes of weight gain and weight loss with detailed assessments of energy balance: Illustrative case studies and clinical recommendations

2014· article· en· W1855048240 on OpenAlexaff
Ryan S. Falck, Robin P. Shook, Gregory A. Hand, Carl J. Lavie, Steven N. Blair

Bibliographic record

VenuePostgraduate Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsVancouver Coastal Health
Fundersnot available
KeywordsMedicineWeight lossWeight changeContext (archaeology)Weight gainPsychosocialBody weightWeight managementEnergy balanceBalance (ability)GerontologyPhysical therapyObesityInternal medicinePsychiatryEcology

Abstract

fetched live from OpenAlex

Extreme weight changes, or changes in weight greater than 10 kg within a 2-year period, can be caused by numerous factors that are much different than typical weight fluctuations. This paper uses two interesting cases of extreme weight change (a female who experienced extreme weight gain and a male who experienced extreme weight loss) from participants in the Energy Balance Study to illustrate the physiological and psychosocial variables associated with the weight change over a 15-month period, including rigorous assessments of energy intake, physical activity (PA) and energy expenditure, and body composition. In addition, we provide a brief review of the literature regarding the relationship between energy balance (EB) and weight change, as well as insight into proper weight management strategies. The case studies presented here are then placed in the context of the literature regarding EB and weight change. This report further supports previous research on the importance of regular doses of PA for weight maintenance, and that even higher volumes of PA are necessary for weight loss. Practitioners should emphasize the importance of PA to their patients and take steps to monitor their patients' involvement in PA.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.054
GPT teacher head0.379
Teacher spread0.325 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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