Assessment of the content reporting for therapeutic exercise interventions among existing randomized controlled trials on knee osteoarthritis
Bibliographic record
Abstract
Purpose/Rationale: Physical exercise interventions for the management of knee osteoarthritis are well known to be effective and accessible forms of rehabilitation and symptom management. However, without adequate reporting of these interventions, accurate replication and clinical use is negatively impacted. OBJECTIVES: The main objective of this article was to assess content reporting using The Consensus on Exercise Reporting Template list and 2016 American College of Sports Medicine guidelines among moderate- to high-quality exercise interventions randomized controlled trials (total score of ≥6/10 on the PEDro scale) involving individuals with knee osteoarthritis. RESULTS: The Consensus on Exercise Reporting Template mean total score for all 47 included randomized controlled trials was 4.42 out of 19, demonstrating generally low quality of reporting. The Consensus on Exercise Reporting Template list and the 2016 American College of Sports Medicine guidelines scores were moderately correlated (based on 95% confidence interval, intraclass correlation coefficient = 0.508) for aerobic interventions only. CONCLUSION: The content analysis of exercise interventions in knee osteoarthritis demonstrated low scores for moderate- to high-quality trials. Improved standardized reporting is recommended to ensure knowledge transfer and replication of effective exercise programs 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 | 0.777 | 0.907 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.021 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".