Evidence-Based Physiotherapy for Acute Low Back Pain: A Composite Clinical Algorithm Synthesized from Seven Recent Clinical Guidelines
Bibliographic record
Abstract
Purpose: To produce a composite evidence-based treatment algorithm for physiotherapy management of acute low back pain (LBP) using current, high-quality, English-language clinical guidelines. Methods: A systematic literature review of library databases and Internet search engines was performed to identify full-text, Englishlanguage clinical guidelines on the physiotherapy treatment of acute LBP. Quality assessment of the guidelines was undertaken by two independent reviewers using the AGREE instrument. Guideline recommendations were synthesized into interventions that were supported by strong, moderate or weak evidence. A composite clinical algorithm for physiotherapy management of acute LBP was developed. Results: Seven guidelines were included. Keeping active, written patient education, manipulation and referral to a spine specialist had strong supporting evidence for the management of acute non-radiating LBP. There were a large number of treatment options with moderate or inconclusive evidence. Bed rest and massage, as stand-alone treatments, had strong evidence of harm for patients with acute non-radiating LBP. Conclusions: Based on current evidence, a composite algorithm was constructed to assist physiotherapists when making treatment decisions for acute LBP. A synthesis of current clinical guideline recommendations provides physiotherapists with readily interpretable guidance for the management of acute LBP and encourages the uptake of best-evidence treatment options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".