A community-based exercise programme for older persons with knee pain using telemedicine
Bibliographic record
Abstract
We explored the feasibility and efficacy of an exercise programme for elderly people with knee pain conducted via videoconferencing. Twenty-two community-dwelling subjects aged 60 years or above with knee pain were recruited from two community centres in Hong Kong. A 12-week exercise programme, including strengthening and balance training, was given via videoconferencing to subjects at both centres, in conjunction with a home-based exercise programme. The outcome measures included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), quadriceps muscle strength, Berg's Balance Scale (BBS) and subjects' degree of acceptance of videoconferencing. Twenty subjects completed the 12-week programme and significant improvements occurred in all domains of the WOMAC score (P <0.003). There was a 44% and a 13% increase in quadriceps muscle strength (P <0.001) and BBS (P <0.001), respectively. Over 80% of the elderly subjects who joined the programme agreed or strongly agreed about all aspects of using videoconferencing. Most of them felt that the system was user-friendly and convenient. Videoconferencing appears to be a useful method of delivering a resistance-training programme for community-dwelling elderly persons with knee pain.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".