OP161 Relationship Between Appropriateness And Arthroplasty Recommendation
Bibliographic record
Abstract
Introduction: We examined relationships between measures of total knee arthroplasty (TKA) “appropriateness” constructs and surgeon TKA recommendations in people with knee osteoarthritis (OA). Although TKA is highly effective, fifteen to thirty percent of recipients report dissatisfaction and/or little or no symptom improvement. More appropriate selection of surgical candidates may improve both patient outcomes and healthcare resource use, but no validated appropriateness criteria exist currently in Canada. Methods: Patients 30 years of age or older with knee OA referred for surgical consultation at two large joint arthroplasty centres in Alberta, Canada were invited to participate. Participants completed a standardized pre-consult questionnaire, which included the following sociodemographics and validated measures of appropriateness constructs for TKA: knee symptoms; non-surgical management; patient readiness for and expectations of TKA; and net patient benefit. Post-consultation, surgeons were asked to confirm knee OA and their recommendation. We used multivariable logistic regression to examine the relationship between measures of appropriateness constructs and receipt of surgeon TKA recommendation. Results: Of 3,009 patients approached, 2,360 completed the questionnaire and 2,064 (sixty-nine percent) were eligible at surgical consultation (mean age 65.7 years, standard deviation 9.1; fifty-nine percent were women); 1,495 (seventy-two percent) were recommended for TKA. The likelihood of receiving a TKA recommendation was independently associated with: knee symptoms (odds ratio [OR] per unit increase in pain intensity, 1.19 (95% confidence interval [CI]: 1.11–1.27)); prior non-surgical OA management (OR for prior knee injection, 1.53 (95% CI: 1.21–1.94)); readiness for surgery (OR if definitely/probably willing to undergo TKA, 3.03 (95% CI: 1.99–4.59)); and TKA expectations (OR outcome “very important”: ability to perform daily activities, 1.40 (95% CI: 1.04–1.88); straighten the knee/leg 1.42 (95% CI: 1.13–1.80); participate in exercise/sports 0.75 (95% CI: 0.58–0.98)). Conclusions: In our cohort of patients with confirmed knee OA who consulted a surgeon for TKA, appropriateness constructs were significantly associated with receipt of a TKA recommendation. Research is ongoing to evaluate the predictive validity of these measures for patient-reported outcomes associated with TKA.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".