Efficacy of a Psychologically-Informed Physiotherapy Intervention in Patients with Chronic Low Back Pain at High Risk of Poor Prognosis: A Pilot and Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
Purpose: To determine the feasibility of a randomized controlled trial (RCT) testing the efficacy of psychologically-informed physiotherapy (PIPT), which includes usual physiotherapy (UP) interventions, compared with UP, and to explore the preliminary effectiveness of the interventions. Method: People with chronic low back pain at high risk of poor prognosis (using the STarT Back screening tool) were recruited and allocated to PIPT or UP. Effectiveness of recruitment strategies, adherence to intervention, risk of contamination, and specific challenges were assessed. Functional capacity, pain, quality of life, kinesiophobia, catastrophization, central sensitization, and self-efficacy were measured at baseline, 6-, 12- and 24-week follow-ups. Results: Forty participants were recruited mainly by diffusing through Laval University's email list, and 10 physiotherapists treated the participants recruited. The retention rate of participants at 24 weeks was 72.5%. Adherence to treatment by participants and physiotherapists was very good. The risk of contamination was low, and the specific challenges identified were modifiable. Significant improvement over time in all clinical variables of interest, except self-efficacy, was observed with no difference between groups. Conclusions: As most success criteria were met, conducting an RCT evaluating PIPT and PU is feasible with modifications. PIPT and UP appear to be similarly effective.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".