Development of quality indicators for hip and knee arthroplasty rehabilitation
Bibliographic record
Abstract
OBJECTIVE: To develop quality indicators (QIs) reflecting the minimum acceptable standard of rehabilitation care before and after elective total hip arthroplasty (THA) and total knee arthroplasty (TKA) for osteoarthritis (OA). METHODS: Informed by high quality evidence and using a modified RAND-UCLA Delphi approach, an 18-member Canadian panel of clinicians, researchers and patients considered 81 proposed QIs (40 for THA, 42 for TKA) addressing rehabilitation before and after elective THA and TKA. Panelists rated QIs for their importance and validity on a 9-point Likert scale through two rounds of online rating interspersed with a moderated and anonymous online discussion forum. Those QIs with median ratings of ≥7 for importance and validity with no disagreement based on the inter-percentile range adjusted for symmetry were included in the final sets. RESULTS: Fifteen panelists from seven provinces and varied practice settings completed the Delphi process. Of the 81 plus one additional QIs (total of 82), 67 (82%) were rated as both important and valid (31 for THA, 36 for TKA). For THA, 14 pre-op, six acute and eight post-acute QIs were accepted. For TKA, 16 pre-op, 10 acute and eight post-acute indicators were accepted. Two of three 'across-continuum' QIs were rated appropriate for both procedures. CONCLUSION: This work represents the first QIs with which to measure, report and benchmark quality of care in patients receiving rehabilitation before and after THA/TKA surgery. The QIs will be further tested for reliability and feasibility before being widely disseminated in clinical settings and used to assess care gaps.
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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 | 0.122 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".