Hip and Knee Total Joint Arthroplasty Online Resources for Patients and Health Care Professionals: A Canadian Environmental Scan
Bibliographic record
Abstract
Purpose: To appraise the quality of publicly available online Canadian resources for patients with hip or knee osteoarthritis considering total joint arthroplasty (TJA) and health care professionals participating in TJA decision-making processes. Method: An environmental scan. Two independent authors appraised: a) patient resources against the International Patient Decision Aids Standards (IPDAS) criteria and the Patient Education Material Evaluation Tool (PEMAT); and b) health care professional resources against six appropriateness criteria for TJA and eight elements of shared decision-making. Analysis was descriptive. Results: Of 84 included resources, 71 were for patients, 11 for health care professionals, and 2 for both. For patient resources, the median number of IPDAS defining criteria met was 2 of 7, median PEMAT understandability score was 83%, and median PEMAT actionability score was 60%. For health care professional resources, the median number of appropriateness criteria was 3 of 6, and the median number of shared decision-making elements was 3 of 8. Conclusions: Only four of 73 patient resources were structured to help patients consider their options and reach a decision based on their preferences. Health care professional resources were limited to traditional criteria for determining TJA appropriateness (evidence of osteoarthritis, use of conservative treatments) and poorly met key elements of shared decision-making.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".