MétaCan
Menu
Back to cohort
Record W3122194084 · doi:10.1002/oby.23074

Associations Between Self‐Reported Weight History and Sarcopenic Obesity in Adults with Knee Osteoarthritis

2021· article· en· W3122194084 on OpenAlexafffund
Kristine Godziuk, Carla M. Prado, Linda J. Woodhouse, Mary Forhan

Bibliographic record

VenueObesity · 2021
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
FundersMitacsAlberta Health Services
KeywordsOsteoarthritisMedicineSarcopenic obesityObesityBody mass indexInternal medicineSarcopeniaYoung adultPhysical therapyPathology

Abstract

fetched live from OpenAlex

Objective The purpose of this study was to examine associations between self‐reported weight history and sarcopenic obesity in adults with advanced knee osteoarthritis (OA). Methods Self‐reported weight history was collected from n = 151 adults (58.9% female) with knee OA and BMI ≥30 kg/m 2 in a cross‐sectional study. Body composition was assessed using dual‐energy x‐ray absorptiometry. Sarcopenic obesity was defined as appendicular skeletal muscle mass, adjusted by BMI, <0.51 kg/m 2 in females and <0.79 kg/m 2 in males; prevalence was 27.2%. Weight gain in the preceding year, weight gain ≥5% of body weight in the past decade, and multiple weight cycling events in life‐span (loss of ≥10 lb [4.5 kg] with regain ≥3 times) were examined using logistic regression (adjusted by age, sex, and %fat mass), with the dependent variable of sarcopenic obesity presence. Results Weight gain in the preceding year was associated with sarcopenic obesity (odds ratio [OR]: 2.45, 95% CI: 1.02‐5.87). No associations were found with weight gain in the past decade (OR: 1.04, 95% CI: 0.43‐2.5) or weight cycling (OR: 0.86, 95% CI: 0.37‐2.01). Conclusions In adults with obesity and advanced knee OA, self‐reported weight gain in the preceding year was associated with sarcopenic obesity. This patient population may benefit from recommendations that prioritize prevention of weight gain.

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)0.000
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 score0.013
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueObesitySame topicNutrition and Health in AgingFrench-language works237,207