A randomised controlled trial of ‘clockwise’ ultrasound for low back pain
Bibliographic record
Abstract
Aims: To examine how the choice of words explaining ultrasound (US) may influence the outcome of physiotherapy treatment for low back pain (LBP).Methods: Sixty-seven patients with LBP < 3 months were randomly allocated to one of three groups – traditional education about US (control group [CG]), inflated education about US (experimental group [EG]) or extra-inflated education about US (extra-experimental group [EEG]). Each patient received the exact same application of US that has shown clinical efficacy for LBP (1.5 Watts/cm2 for 10 minutes at 1 Megahertz, pulsed 20% over a 20 cm2 area), but received different explanations (CG, EG or EEG). Before and immediately after US,measurements of LBP and leg pain (numeric rating scale), lumbar flexion (distance to floor) and straight leg raise (SLR) (inclinometer) were taken. Statistical analysis consisted of mixed-factorial analyses of variance and chi-square analyses to measure differences between the three groups, as well as meeting or exceeding minimal detectable changes (MDCs) for pain, lumbar flexion and SLR.Results: Both EG and EEG groups showed a statistically significant improvement for SLR (p < 0.0001), while the CG did not. The EEG group participants were 4.4 times (95% confidence interval: 1.1 to 17.5) more likely to improve beyond the MDC than the CG. No significant differences were found between the groups for LBP, leg pain or lumbar flexion.Conclusion: The choice of words when applying a treatment in physiotherapy can alter the efficacy of the treatment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".