Modelling Knee Range of Motion Post Arthroplasty: Clinical Applications
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
PURPOSE: To model change in knee range of motion (ROM) post total knee arthroplasty (TKA) and to show how this information can be incorporated into clinical decision making. METHOD: We applied a variable-occasion repeated-measures study design. Patients' knee flexion and extension ROM were assessed pre- and post arthroplasty over the ensuing 60 weeks. We examined change in ROM post TKA using linear and nonlinear mixed-effects modelling, and examined whether age, body mass index, prearthroplasty ROM, and gender were determinants of recovery in post-arthroplasty ROM. RESULTS: Of 93 eligible patients, 74 provided pre- and post-arthroplasty data. A random intercept nonlinear model fit the flexion data best, and a random intercept linear model fit the extension data best. Pre-arthroplasty ROM was found to be a determinant of recovery in ROM post arthroplasty. This finding was common to both flexion and extension models. CONCLUSIONS: Our study showed that the greatest improvement for knee ROM took place during the first 12 weeks post arthroplasty. Of the variables examined, only pre-arthroplasty ROM was a determinant of outcome (p<0.05). The study results provide clinicians with data to determine expected rates of improvement for patients as well as the projected maximum ROM, facilitating improved clinical decision making.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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