The Ottawa panel clinical practice guidelines for the management of knee osteoarthritis. Part three: aerobic exercise programs
Bibliographic record
Abstract
OBJECTIVES: To identify effective aerobic exercise programs and provide clinicians and patients with updated, high-quality recommendations concerning traditional land-based exercises for knee osteoarthritis. METHODS: A systematic search and adapted selection criteria included comparative controlled trials with strengthening exercise programs for patients with knee osteoarthritis. A panel of experts reached consensus on the recommendations using a Delphi survey. A hierarchical alphabetical grading system (A, B, C+, C, D, D+, or D-) was used, based on statistical significance ( P < 0.5) and clinical importance (⩾15% improvement). RESULTS: The five high-quality studies included demonstrated that various aerobic training exercises are generally effective for improving knee osteoarthritis within a 12-week period. An aerobic exercise program demonstrated significant improvement for pain relief (Grade B), physical function (Grade B) and quality of life (Grade C+). Aerobic exercise in combination with strengthening exercises showed significant improvement for pain relief (3 Grade A) and physical function (2 Grade A, 2 Grade B). CONCLUSION: A short-term aerobic exercise program with/without muscle strengthening exercises is promising for reducing pain, improving physical function and quality of life for individuals with knee osteoarthritis.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".