Impact of a Surgical Screening Clinic for Patients With Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate a surgical screening clinic for patients with knee osteoarthritis (OA) referred for total knee arthroplasty (TKA) and explore management before referral. DESIGN: Descriptive study using retrospective chart review. SETTING: Large Canadian teaching hospital. PARTICIPANTS: Patients with knee OA referred for TKA over a 1-year period. INTERVENTIONS: Patients underwent standardized assessment by physicians who practice sport medicine to determine eligibility for surgical consultation. MAIN OUTCOME MEASURES: Proportion of patients deemed eligible for surgical consultation and undergoing TKA, and conservative management options tried before clinic referral. RESULTS: Of the 327 patients, 172 (52.6%) were referred to the surgeon, of whom 76% underwent TKA. Options used before referral were medications (92.0%), injections (41.3%), and physiotherapy (34.9%). Patients referred to the surgeon, compared with those who were not, were more likely to have met all referral criteria (86.5% vs 33.3%, P < 0.001), tried 3 or more options (70.9% vs 49.7%, P < 0.01), used injections (58.7% vs 32.3%, P < 0.001), scored higher on the Hip-Knee Priority Tool (45 vs 24, P < 0.001), and had been referred by a physician who practices sport medicine (88.2% vs 46.2%, P < 0.001). CONCLUSIONS: Orthopedic surgical screening by trained physicians using standardized tools halved the number of surgical consultations. Few conservative management options were tried before referral, indicating the need to enhance presurgical care for patients with knee OA.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".