Postoperative Care Navigation for Total Knee Arthroplasty Patients: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To establish the efficacy of motivational interviewing-based postoperative care navigation in improving functional status after total knee arthroplasty (TKA) and to identify subgroups likely to benefit from the intervention. METHODS: We conducted a parallel randomized controlled trial in TKA recipients with 2 arms: postoperative care with frequent followup by a care navigator or usual care. The primary outcome was the difference between the arms in Western Ontario and McMaster Universities Osteoarthritis Index function score change, over 6 months postsurgery. We performed a preplanned subgroup analysis of differential efficacy by obesity and exploratory subgroup analyses on sex and pain catastrophizing. RESULTS: We enrolled 308 subjects undergoing TKA for osteoarthritis. Mean ± SD preoperative function score was 41 ± 17 (0-100 scale, where 100 = worst function). At 6 months, subjects in the navigation arm improved by mean ± SD 30 ± 16 points compared to 27 ± 18 points in the usual-care arm (P = 0.148). Participants with moderate to high levels of pain catastrophizing were unlikely to benefit from navigation compared to those with lower levels of pain catastrophizing (P = 0.013 for interaction). CONCLUSION: Subjects assigned to the navigation intervention did not demonstrate greater functional improvement compared to those in the control group. The negative overall result could be explained by the large effect on functional improvement of TKA itself compared to the smaller, additional benefit from care navigation, as well as by potential differential effects for subjects with moderate to high degrees of pain catastrophizing. Greater focus on developing programs for reducing pain catastrophizing could lead to better functional outcomes following 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".