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Record W4391755402 · doi:10.1016/j.joca.2024.02.003

Obesity and body mass index: Past and future considerations in osteoarthritis research

2024· article· en· W4391755402 on OpenAlexafffund
Kristine Godziuk, Gillian Hawker

Bibliographic record

VenueOsteoarthritis and Cartilage · 2024
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersObesity CanadaAlberta Innovates
KeywordsObesityBody mass indexWeight stigmaOsteoarthritisMedicineGerontologyOverweightPhysical therapyAlternative medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Obesity is an important topic for the osteoarthritis (OA) scientific community. However, the predominant use of body mass index (BMI) to define obesity in OA research is associated with uncertainties and limitations. These include an inability to discern fat and muscle mass, account for sex-differences in fat distribution, or identify adiposity-related health impairments. A focus on BMI in OA research may influence weight bias in clinical practice and impact disparities in access to effective OA treatments. To ensure that our understanding and approaches to improve health outcomes for individuals with or at risk for OA continues to advance in the next decade, future research will need to consider alternative measures beyond BMI for obesity identification and align with evolving obesity science. OA researchers must be aware of issues associated with weight stigma and work to minimize negative generalizations based on BMI.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.396
Teacher spread0.354 · 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 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

Citations53
Published2024
Admission routes2
Has abstractyes

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