Determining Appropriateness of Total Joint Arthroplasty for Hip and Knee Osteoarthritis: A Patient‐Centred Conceptual Model
Bibliographic record
Abstract
INTRODUCTION: Total joint arthroplasty (TJA) for osteoarthritis is one of several treatment options with benefits and harms that patients value differently. However, the process for determining TJA appropriateness does not sufficiently acknowledge patient perspectives. The aim of this paper is to propose an evidence-informed patient-centred conceptual model for elective TJA appropriateness for hip and knee osteoarthritis. METHODS: Our interprofessional team developed a conceptual model for determining the appropriateness of adults considering elective TJA. The model was informed by a review of the evidence, a qualitative study we conducted with adults who underwent TJA for osteoarthritis to determine barriers and facilitators to the use of appropriateness criteria, and the research and clinical experience of team members. RESULTS: Appropriateness is providing health services (e.g., TJA) with net benefits to the right patient at the right time. The proposed Patient-centred Elective TJA Appropriateness Conceptual Model involves three key steps. First, assess adults with osteoarthritis to determine eligibility for TJA. Second, acknowledge the patient's informed preferences including their expectations and goals. Third, explore and support their mental and physical readiness for TJA. Given that osteoarthritis is a chronic condition, these steps can be revisited over time with patients. DISCUSSION AND CONCLUSION: Our proposed conceptual model reconceptualises the appropriateness of TJA to be more patient-centred. Hence, this approach has the potential to be a more inclusive approach and ensure patients undergoing TJA are eligible, ready to proceed, and achieve what matters most to them. Future research is needed to test and validate the model.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".