The STAK tool: evaluation of a new device to treat arthrofibrosis and poor range of movement following total knee arthroplasty and major knee surgery
Bibliographic record
Abstract
Aims This study aims to evaluate a new home medical stretching device called the Self Treatment Assisted Knee (STAK) tool to treat knee arthrofibrosis. Methods 35 patients post-major knee surgery with arthrofibrosis and mean range of movement (ROM) of 68° were recruited. Both the STAK intervention and control group received standard physiotherapy for eight weeks, with the intervention group additionally using the STAK at home. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Oxford Knee Scores (OKS) were collected at all timepoints. An acceptability and home exercise questionnaire capturing adherence was recorded after each of the interventions. Results Compared to the control group, the STAK intervention group made significant gains in mean ROM (30° versus 8°, p < 0.0005), WOMAC (19 points versus 3, p < 0.0005), and OKS (8 points versus 3, p < 0.0005). The improvements in the STAK group were maintained at long-term follow-up. No patients suffered any complications relating to the STAK, and 96% of patients found the STAK tool ‘perfectly acceptable’. Conclusion The STAK tool is effective in increasing ROM and reducing pain and stiffness. Patients find it acceptable and adherence to treatment was high. This study indicates that the STAK tool would be of benefit in clinical practice and may offer a new, cost-effective treatment for arthrofibrosis. Cite this article: Bone Joint Open 2020;1-8:465–473.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".