O36. Improving the Effectiveness of Exercise Therapy for Older Adults with Knee Pain: A Pragmatic Randomized Controlled Trial
Bibliographic record
Abstract
Background: Exercise for knee pain attributable to OA is recommended as a core treatment in clinical guidelines but it is unclear how exercise is best delivered to optimize patient outcomes. Methods: The BEEP trial (ISRCTN93634563) was a parallel, pragmatic randomized, controlled trial investigating the effectiveness of two physiotherapy-led exercise interventions compared with usual physiotherapy care (UC), for pain and function in older adults with knee pain. Adults aged ≥45 years with knee pain attributable to OA from 65 general practices in England were randomized using computer-generated blocks of size 3 to UC (up to four treatment sessions of advice and exercise over 12 weeks); Individually Tailored Exercise (ITE; an individualized, supervised and progressed lower-limb exercise programme in 6–8 treatment sessions over 12 weeks); or Targeted Exercise Adherence (TEA; supporting the transition from lower limb exercise to general physical activity in 8–10 treatment contacts over 6 months). 47 physiotherapists from 5 National Health Service physiotherapy services delivered treatment: 15 delivered UC, 17 ITE and 15 TEA. Primary outcomes were self-reported pain and physical function [Western Ontario McMaster Osteoarthritis Index (WOMAC)] at 6 months. A range of secondary outcomes were measured at baseline and at 3, 6, 9 and 18 months. Participants and physiotherapists were not blind to allocation, but outcome data collection and analyses were conducted blind to allocation. Primary analysis was by intention-to-treat; sensitivity analysis was per protocol analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".