Revisiting patient satisfaction following total knee arthroplasty: a longitudinal observational study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Total knee arthroplasty (TKA) is the most common joint replacement surgery in Canada. Earlier Canadian work reported 1 in 5 TKA patients expressing dissatisfaction following surgery. A better understanding of satisfaction could guide program improvement. We investigated patient satisfaction post-TKA in British Columbia (BC). METHODS: A cohort of 515 adult TKA patients was recruited from across BC. Survey data were collected preoperatively and at 6 and 12 months, supplemented by administrative health data. The primary outcome measure was patient satisfaction with outcomes. Potential satisfaction drivers included demographics, patient-reported health, quality of life, social support, comorbidities, and insurance status. Multivariable growth modeling was used to predict satisfaction at 6 months and change in satisfaction (6 to 12 months). RESULTS: We found dissatisfaction rates ("very dissatisfied", "dissatisfied" or "neutral") of 15% (6 months) and 16% (12 months). Across all health measures, improvements were seen post-surgery. The multivariable model suggests satisfaction at 6 months is predicted by: pre-operative pain, mental health and physical health (odds ratios (ORs) 2.65, 3.25 and 3.16), and change in pain level, baseline to 6 months (OR 2.31). Also, improvements in pain, mental health and physical health from 6 to 12 months predicted improvements in satisfaction (ORs 1.24, 1.30 and 1.55). CONCLUSIONS: TKA is an effective intervention for many patients and most report high levels of satisfaction. However, if the TKA does not deliver improvements in pain and physical health, we see a less satisfied patient. In addition, dissatisfied TKA patients typically see limited improvements in mental health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it