The PROGRESS trial three years later: Clear and accurate interpretations of studies are needed
Bibliographic record
Abstract
Physiotherapy compared with advice for low back painTargeting "physical factors" alone is not evidence based practice Editor-Frost et al's conclusion that "routine physiotherapy" based on physical factors was no more effective than one session of assessment and advice from a physiotherapist in the management of low back pain is not surprising. 1 But the defensive nature of the responses to this research is. 2 3 This defensiveness arises partly from the perceived rivalry between healthcare professions managing low back pain and the attention grabbing headlines used.In recent years the evidence base has highlighted that low back pain is a multifaceted phenomenon incorporating physical impairment, psychological distress, and social interruption.Thus the effective biopsychosocial management of low back pain should reflect its multifaceted nature and not just focus on the "physical factors," as was done by Frost et al.Being an evidence based practitioner should entail identifying and managing patients' risk factors because risk factors are clinical predictors of outcome and efforts to manage them may reduce the burden of low back pain for those who consult physiotherapists.Because of the recurrent nature of low back pain, talk of a "cure" is unrealistic.Thus the Physiotherapy Pain Association emphasises that patients should be taught skills to self manage their low back problem so that in the long term they are less likely to experience pain related disability and depression, thus improving their quality of life.Receiving passive treatments focusing on physical factors, which show only slight short term benefits, is not in the personal or economic interest of patients with low back pain.As is highlighted by the responses to the study by Frost et al, 3 beliefs about treatment preferences for low back pain vary across professions and can be traced to beliefs about the cause of the problem.
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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 | 0.071 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".