A feasibility study of brain-targeted treatment for people with painful knee osteoarthritis in tertiary care
Bibliographic record
Abstract
Purpose: To assess the feasibility and clinical impact of brain-targeted treatment (BT; aiming to target sensorimotor processing) in knee osteoarthritis patients attending tertiary care. Methods: Randomized replicated case series. The study involved three phases, each of 2 weeks duration: (1) no-treatment phase; (2) BT phase (left/right judgments and touch discrimination training); and (3) usual care (education, strengthening, and stretching training). Primary outcomes were: timely recruitment; number of participants completing the interventions; treatment compliance and barriers; follow-up rates; and treatment impact on pain and function. Fear-avoidance beliefs and clinical measures of cortical body representation (tactile acuity and left/right judgment performance) were secondary outcomes. Results: A total of 5% (19/355) of all assessed patients were eligible to participate and of these, 58% (11/19) agreed to participate. Ten patients completed the study, and 9 were successfully followed up, with treatment compliance varying between interventions. Compliance was poor for the touch discrimination component of BT. No significant effects were observed for pain relief or knee function after any treatment. A positive impact of treatment was found for fear-avoidance beliefs (usual care vs. washout, p = 0.007; BT vs. washout, p = 0.029) and left/right judgment accuracy (usual care vs. washout; p = 0.006). Conclusions: Clear barriers were identified to implementing BT in tertiary care for knee osteoarthritis. Access to all available services (especially the use of interpreters), and treatment options that do not require additional assistance to perform (e.g., touch discrimination training) represent the main lessons learned.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".