Is a Combination of Exercise and Dry Needling Effective for Knee OA?
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of adding dry needling (DN) to an exercise program on pain intensity and disability in patients with knee osteoarthritis. DESIGN: Double-blind randomized clinical trial with one-year follow-up. SETTING: Older adults in a multicenter study. SUBJECTS: Sixty-two patients with knee osteoarthritis were randomly allocated into one of two groups: exercise plus DN (exercise + DN; N = 31) or exercise plus sham DN (exercise + sham DN; N = 31). METHODS: Participants received six sessions of either DN or sham DN over the leg muscles related to knee pain from osteoarthritis plus a supervised exercise program. We evaluated between-group differences in terms of the numerical pain rating scale (NPRS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score. We used the EuroQol Group 5-Dimension Self-Report Questionnaire, Barthel Index, Timed Up & Go Test, and Global Rating of Change Scale to examine between-group differences for health-related quality of life, functional status evaluation, balance assessment, and clinical progress, respectively. RESULTS: The groups were not different in terms of pain intensity (0.32 points, 95% confidence interval [CI] = -1.12 to 1.18, P = 0.92) or WOMAC score (0.29 points, 95% CI = -6.16 to 6.74, P = 0.92) at one year. Both groups presented within-group differences at all follow-up periods (F = 28.349, P < 0.0001, ηp2 = 0.32) on secondary outcomes. Nevertheless, 90.3% of the DN group had reduced medication consumption vs only 26.3% in the sham DN group. CONCLUSIONS: The inclusion of DN to an exercise program does not reduce pain or disability in patients with knee osteoarthritis.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".