Manual Therapy With Cryotherapy Versus Kinesiotherapy With Cryotherapy for Knee Osteoarthritis: A Randomized Controlled Trial.
Bibliographic record
Abstract
CONTEXT: Manual therapy and kinesiotherapy are used for knee osteoarthritis (OA). Yet, a clear evidence of the effects of manual therapy versus kinesiotherapy on knee OA is limited. The addition of cryotherapy to manual therapy or to kinesiotherapy may enhance the health benefits in patients with knee OA. OBJECTIVE: The study intended to evaluate the efficacy of manual therapy combined with cryotherapy versus kinesiotherapy combined with cryotherapy for patients with knee OA. DESIGN: The research team designed a randomized, controlled trial. SETTING: The study occurred in the Physiotherapy Outpatient Department of the Regional Hospital (Sandomierz, Poland). PARTICIPANTS: The participants were 128 females and males with knee OA, aged 40 to 80 y, who were patients in the department at the hospital. INTERVENTION: The participants were randomly assigned to an intervention group that received manual therapy combined with cryotherapy, the MT-C group (n = 64), or to a control group, which received kinesiotherapy combined with cryotherapy, the KIN-C group (n = 64). The participants in both groups received 10 treatments, 2 per wk for 5 wk. OUTCOME MEASURES: The primary outcome was measured using a visual analog scale pain ratings. The secondary outcome measured the quality of life using the Western Ontario and McMaster Universities questionnaire, knee extension, and flexion range of motion using the goniometer, and functional capacity using the 6-min walk test. RESULTS: After the treatments, the intervention group had significantly lower scores than the control group for pain, as well as significantly higher scores for quality of life, range of motion of the affected knee, and functional capacity. CONCLUSION: The patients achieved better health benefits from manual therapy when it was combined with cryotherapy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".