Improving osteoarthritis detection in the community: Pharmacist identification of new, diagnostically confirmed osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis (OA) is the most common arthritis and a leading cause of disability. Many persons with knee OA are not diagnosed and not referred for treatment. Therefore, identification of patients with knee pain who have undiagnosed OA needs to be improved. Our objective was to determine if pharmacists, using a simple screening questionnaire, can identify individuals with previously undiagnosed knee OA. METHODS: Patients with knee pain and no previous diagnosis of knee OA were recruited by community pharmacists who used a simple questionnaire (<10 minutes to complete) to determine likelihood of knee OA. Patients who were likely to have knee OA were referred for a standardized knee examination and radiograph. RESULTS: Of the 411 patients screened by pharmacists, 274 were eligible. Of these, 44 declined, 35 were ineligible (18 had a previous OA diagnosis,16 had other inflammatory conditions, and 1 was excluded for other reasons), and 1 died. The remaining 194 were mostly female (62%) with a mean age of 62 years and were mostly white (86%). Body mass index (BMI) was classified as normal (18.5-24.9 kg/m(2)) in 29%, overweight (25.0-29.9 kg/m(2)) in 45%, and obese (>30.0 kg/m(2)) in 26%. Of those examined, 190 (98%) of 194 met the American College of Rheumatology clinical criteria for knee OA. The radiographic results revealed that most participants likely had mild OA. CONCLUSION: Pharmacists administering a simple screening questionnaire can identify >80% of patients with knee pain who have undiagnosed knee OA. Based on radiographs and BMI, much of this OA is early and may be amenable to intervention.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".