Power Point Therapy: An Effective and Simple Treatment for Subacute Back Pain – A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: Subacute low back pain is a frequent problem with the danger of chronification. Conventional treatment options are not always effective. Power Point therapy (PPT) is a novel approach that uses reflexological insights and can be easily applied by practitioners and patients. METHODS: Randomized, active controlled study comparing 10 units of PPT of 10 min each, with 10 units of standard physiotherapy of 30 min each. Outcomes were functional scores (Roland Morris Disability, Oswestry, McGill Pain Questionnaire, Linton-Halldén - primary outcome) and health-related quality of life (SF-36), as well as blinded assessments by clinicians (secondary outcome). RESULTS: Eighty patients consented and were randomized, 41 to PPT, 39 to physiotherapy. Measurements were taken at baseline, after the first and after the last treatment (approximately 5 weeks after enrolment). Multivariate linear models of covariance showed significant effects of time and group (p < 0.001) and for the quality of life variables also a significant interaction of time by group (p < 0.001). Clinician-documented variables showed significant differences at follow-up (p = 0.05 to p < 0.0001). DISCUSSION: Both physiotherapy and PPT improve subacute low back pain significantly. PPT is likely more effective and should be studied further.
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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 | 0.005 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".