Modelling Knee Range of Motion Post Arthroplasty: Clinical Applications
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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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".