Improvement following total knee replacement surgery: Exploring preoperative symptoms and change in preoperative symptoms
Bibliographic record
Abstract
OBJECTIVE: To determine whether changes in preoperative osteoarthritis (OA) symptoms are associated with improvement after total knee replacement (TKR) and to identify predictors of clinically significant improvement. METHODS: Data on Osteoarthritis Initiative participants who were annually assessed and underwent TKR were included. T0 was the assessment prior to TKR while T-1 was the assessment prior to that. T+2 was the second assessment after TKR. We compiled data on the Western Ontario and McMaster Universities OA Index (WOMAC), OA-related symptoms, and radiographic severity. We defined clinically significant improvement as improvement in WOMAC total score ≥ to the minimal important difference (MID) (0.5 SD of mean change) between T0 and T+2 and also considered other definitions of improvement. Logistic regression models were performed to evaluate the relationship between improvement and preoperative measures. RESULTS: Improved (n = 211) compared to unimproved (n = 58) patients had greater worsening of their WOMAC pain (p = 0.002) and disability (p < 0.001) from T-1 to T0. Preoperative measures as predictors of improvement included higher WOMAC disability (OR = 1.08, p < 0.001), presence of chronic OA symptoms in the surgical knee (OR = 5.77, p = 0.033), absence of OA-related symptoms in the contralateral knee (OR = 9.25, p < 0.001), exposure to frequent knee bending (OR = 3.46, p = 0.040), and having a Kellgren-Lawrence x-ray grade of ≥2 in the contralateral knee (OR = 4.71, p = 0.010). CONCLUSIONS: More than 75% of participants had improvement after TKR. Improved patients were more likely to have escalation of OA pain and disability prior to surgery than unimproved patients. Other preoperative measures predicted improvement after TKR.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".