After patients are diagnosed with knee osteoarthritis, what do they do?
Bibliographic record
Abstract
OBJECTIVE: To learn more about the health services and products that patients use after receiving a diagnosis of knee osteoarthritis (OA), as well as the trajectory of their health-related quality of life (HRQOL). METHODS: Using a simple screening survey, community pharmacists identified 194 participants with previously undiagnosed knee OA. Of these participants, 190 were confirmed to have OA on further investigation. At baseline and 1, 3, and 6 months after diagnosis, a survey was administered to assess health services, product use, and HRQOL, including the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Medical Outcomes Study Short Form 36 (SF-36) health survey, the Paper Adaptive Test (PAT-5D-QOL), and the Health Utilities Index Mark 3. RESULTS: With a mean age of 63 years, participants were mostly women, white, and overweight. By 6 months, more than 90% of the participants had visited their family physician to discuss their OA, and more than 50% of participants took either prescription or nonprescription analgesics. In addition, three-quarters of the participants started exercising, one-third initiated activity aids, and one-third had started natural medicine products. At 6 months compared with baseline, significant improvements were seen in the SF-36 physical component summary (P = 0.001) and bodily pain domain scores (P = 0.02), the PAT-5D-QOL overall, pain, and usual daily activities scores (P < 0.001 for all), and the WOMAC total, pain, and function scores (P < 0.001 for all). CONCLUSION: Within 6 months of receiving a diagnosis of knee OA, participants made several lifestyle interventions, often without the advice of a health professional, and saw improvements in their pain and function.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".