MétaCan
Menu
Back to cohort
Record W3138243399 · doi:10.33590/emj/10312932

Obesity: The Impact on Host Systems Affecting Mobility and Navigation through the Environment

2019· article· en· W3138243399 on OpenAlexafffund
David A. Hart, Walter Herzog, Raylene A. Reimer, Jaqueline Lourdes Rios, Kelsey H. Collins

Bibliographic record

VenueEuropean Medical Journal · 2019
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of CalgaryAlberta Health Services
FundersKillam TrustsCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAlberta Health Services
KeywordsObesityAffect (linguistics)MedicinePhysical medicine and rehabilitationPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Obesity is known to affect a high percentage of both adults and children in developed countries. Individuals with obesity are at risk of developing a number of comorbidities, as well as metabolic syndrome, which can create a low-grade systemic inflammatory state that further exacerbates the risk of developing comorbidities. Two systems that are susceptible to obesity-related effects are the musculoskeletal system, which contributes to mobility via the bones, muscles, tendons, and joints, and the eye, which contributes to mobility via fidelity of navigation through the environment. Subsequently, the loss of integrity in these systems can lead to sedentary behaviour, inability to exercise, and increased risk of developing cardiovascular and respiratory diseases, loss of cognition, and falls. This review focusses on the impact of obesity on elements of the musculoskeletal system and the eye, with particular focus on the involvement of inflammation and how this may affect mobility and navigation. Finally, the use of prebiotics in altering the inflammatory state associated with obesity via the gut microbiome is discussed as one approach to address issues related to mobility and navigation.

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)0.006
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 score0.548
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.020
GPT teacher head0.314
Teacher spread0.294 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueEuropean Medical JournalSame topicFatty Acid Research and HealthFrench-language works237,207