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Record W2625330961 · doi:10.15406/aowmc.2017.06.00174

Obesity in an Ageing Population: A Proposed Multidisciplinary Intervention Model for Supporting Cognitive Performance and Physical Function in Obese Seniors

2017· article· en· W2625330961 on OpenAlexaff
Amanda Baker

Bibliographic record

VenueAdvances in Obesity Weight Management & Control · 2017
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of OttawaBruyère
Fundersnot available
KeywordsMultidisciplinary approachCognitionGerontologyIntervention (counseling)ObesityAgeingPopulation ageingPopulationPhysical activityMedicinePsychologyPhysical medicine and rehabilitationPhysical therapyPsychiatryEnvironmental healthSociologyInternal medicine

Abstract

fetched live from OpenAlex

Obesity in ageing adults is a precursor for increased risk of various diseases and chronic health conditions including cognitive impairment and poorer mobility thereby threatening quality of life and independent living for many seniors.Fortunately, weight loss and weight management interventions have been recognized as a means to help improve both physical function and cognition in obese older persons.Based on the literature, we propose that programs and services embodying a multidisciplinary team who focus on education and training related to physical activity, diet, and pharmaceuticals be offered to optimally support weight loss, improve mobility and enhance cognition for this population.Future intervention research is warranted to test this multidisciplinary approach in order to help develop suitable interventions and preventative measures to reduce or delay decline in mobility and the onset of cognitive impairment and dementia in obese seniors.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.321
Teacher spread0.307 · 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 designTheoretical or conceptual
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

Citations1
Published2017
Admission routes1
Has abstractyes

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