Arthritis-Specific Health Beliefs Related to Aging Among Older Male Patients With Knee
Bibliographic record
Abstract
Background. Disease-specific beliefs may impact patients ’ perceptions of the efficacy of various treatment options, thus, it is important to understand these beliefs. We examined the relationship between patients ’ demographic characteristics and arthritis-specific beliefs related to aging. Methods. We performed a cross-sectional survey of 591 elderly primary care patients, who had symptomatic osteoarthritis (OA) of the knee and/or hip, at the Louis Stokes VA Medical Center in Cleveland, Ohio. Data were collected on age, race, educational level, income, and whether patients agreed or disagreed with four statements regarding aging and arthritis. We also assessed OA symptom severity using the Western Ontario McMaster Universities Index (WOMAC) and depressive symptoms using the Geriatric Depression Scale. We used logistic regression analyses to examine relationships between patients ’ age, race, and educational level and arthritis-specific health beliefs, while adjusting for OA symptom severity, radiographic confirmation of OA, OA joint burden, depressive symptoms, and income. Results. Patients 70 years old or older, as compared to patients 50–59 years old, were more likely to believe that: arthritis is a natural part of growing old; people should expect that when they get older, they won’t be able to walk as well, and people should expect to live with pain as they grow older. Conclusion. Among older, male veterans, health beliefs regarding the relationship between aging and arthritis vary by age. Clinicians should consider these differences when discussing treatment strategies with their patients with knee
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".