Can selected physiotherapeutic techniques really help in treating back pain and improving the quality of life of advanced cancer patients: a randomised controlled study
Bibliographic record
Abstract
Background: The available literature lacks studies using the KinesioTaping (KT) method and the hold-relax (HR) technique in working with advanced cancer patients. Patients and methods: The study involved 72 patients (38 women and 34 men), diagnosed with advanced cancer. Patients were randomly assigned either to the KT group (exercise programme and KT), HR group [exercise programme and the HR technique of the proprioceptive neuromuscular facilitation (PNF) method] or C group (a control group, exercise programme). To assess selected parameters the Numerical Rating Scale (NRS) scale and Edmonton Symptom Assessment System — revised (ESAS-r) were used. The physiotherapeutic programme lasted three weeks and took place 5 days a week for 30 minutes within each group. Additionally, in the KT group, kinesio tapes were applied on the paraspinal muscles of the lumbar spine. In the HR group the therapy with the hold-relax technique was applied. Results: There was a statistically significant decrease in pain in all groups and an improvement in the quality of life in patients from both experimental groups. Those changes were significantly greater in the KT group than in both the C group and the HR group. Conclusions: Both KT and HR techniques of the PNF method are effective in reducing pain and improving the quality of life in the examined advanced cancer patients, however, KT has a stronger impact.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| 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".