OSTEOARTHRITIS AND DEPRESSION IN A MALE VA POPULATION
Bibliographic record
Abstract
Abstract Osteoarthritis (OA) is a leading cause of disability among older adults. By 2050, approximately 60 million will suffer from arthritis adding up to a total societal cost of $65 billion. Chronic illnesses resulting in pain, and functional decline have been associated with depression in previous studies. The primary goal of this study is to investigate whether OA severity, as measured by the Western Ontario McMasters Arthritis Composite (WOMAC), impacts reported levels of depression and to what degree clinical and sociodemographic variables play a part. A causal model was developed and tested examining the antecedents of OA disease severity and depression. Information on clinical, demographic, socioeconomic, and psychosocial variables was collected on 596 male Veterans with moderate to severe symptomatic OA of the knee\hip. A Confirmatory Factor Analysis was conducted to determine the factor structure of the WOMAC. A 2nd order three factor solution (pain, stiffness, and function) fit the data well (TLI of .94, a CFI of .94 and a RMSEA of .058). The results of the Structural Equation Model reveal a final model that fit the data well (TLI of .95, a CFI of .97 and a RMSEA of .047). Depression was predicted by higher WOMAC scores (beta=.37 , p<.01); higher levels of comorbidity (beta= .11, p<.05); younger age (beta= -.29, p<.01); being white (beta=-.11, p<.05); lower levels of income (beta= -.12, p<.05); lower levels of religiosity (beta= 11, p<.05). Clinicians should be aware of the impact of disease severity when treating OA patients with depression.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".