Prevalence and burden of osteoarthritis amongst older people in Colombia: Results from national survey of health, wellbeing, and aging (SABE Colombia)
Bibliographic record
Abstract
Objectives To investigate the prevalence of osteoarthritis (OA) and to identify factors related with person-level risk factors among older people in Colombia. Study design This study used cross-sectional data from 23.694 adults aged ≥60 or older (median: 70.8 years, 57.3 % women) living in rural and urban communities from the National Survey of Health, Wellbeing and Aging in Colombia (SABE Colombia, according to its initials in Spanish). Methods Logistic regression was used to determine associations between the presence of OA and a range of sociodemographic, health-related, functional, biomarkers, and social/environmental variables. Results The overall prevalence of OA was 26 % (women-36.5 %; men-14.9 %). Prevalence increased with age and in mestizo ethnicity placed in urban areas. On multivariable analysis, OA was significantly associated with older age, female gender, multimorbidity, fair/poor self-perceived health, a higher body mass index (BMI), a greater number of physical limitations, and perceived safety/security problems in the neighborhood. In particular, there was a strong association between multimorbidity and the presence of OA (OR = 4.97 (95 % CI 4.46, 5.53)). An inverse association between HDL cholesterol levels and the prevalence of OA was observed. Triglyceride levels showed a significant trend. Conclusions OA is a common and multifaceted condition, with a comparable prevalence of self-reported OA in Colombia with similar populations elsewhere. Assessment and management should focus on potentially modifiable factors such as BMI, multimorbidity, metabolic syndrome, physical limitations, mobility disabilities, and safety/security problems in the neighborhoods. More research is required to understand the complex interrelationships between these and other risk-associated variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".