Self‐Reported Knee Instability Before and After Total Knee Replacement Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and burden of pain and activity limitations associated with retaining presurgery self-reported knee instability 6 months after total knee replacement (TKR) surgery and to identify early potentially modifiable risk factors for retaining knee instability in the operated knee after TKR surgery. METHODS: A secondary analysis was performed using measures obtained from 390 participants undergoing primary unilateral TKR and participating in a randomized clinical trial. Self-reported knee instability was measured using 2 items from the Activities of Daily Living Scale of the Knee Outcome Survey. Outcome measures were knee pain (range 0-20) and physical function (range 0-68) on the Western Ontario and McMaster Universities Arthritis Index (WOMAC), stair-climb power, 50-foot walk time, knee range of motion, and isometric knee flexion and extension strength. RESULTS: In this study, 72% of participants reported knee instability just prior to surgery, with 32% retaining instability in the operated knee 6 months after surgery. Participants retaining operated knee instability had significantly more knee pain and activity limitations 6 months after surgery, with mean ± SD WOMAC scores of 4.8 ± 3.7 and 17.5 ± 11.1, respectively, compared to participants without knee instability, with 2.9 ± 3.1 and 9.8 ± 9.2. The multivariable predictor model for retained knee instability included a high comorbidity score (>6), low stair-climb power (<150 watts), more pain in the operated knee (>7 of 20), and younger age (<60 years). CONCLUSION: Self-reported knee instability is highly prevalent before and after TKR surgery and is associated with a considerable burden of pain and activity limitation in the operated knee. Increasing lower extremity muscle power may reduce the risk of retaining knee instability after TKR surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 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".