People Strongly Value Physical Therapies for Low Back Pain Over Doing Nothing, Even When Effects Are Very Small: A Discrete Choice Experiment
Bibliographic record
Abstract
OBJECTIVE: To explore factors that influence patient preferences for recommended physical therapies for low back pain. DESIGN: Discrete choice experiment. METHODS: Respondents were randomized to a block of 12 choice tasks and asked to choose between two physical therapies or no treatment. Characteristics of the physical therapies varied between choice tasks and included type (exercise, advice and education, or clinician-directed treatment), effectiveness, time for symptoms to improve, costs, risk of side effects, and treatment duration. Choices were analyzed using a mixed logit model. Latent class analysis examined preference heterogeneity. To measure decision trade-offs, we estimated the smallest worthwhile effect and the “willingness to pay” value. RESULTS: A total of 697 Australians reporting a history of low back in the last year completed all choice tasks. Respondents showed a strong preference for taking any nonpharmacologic care option over no treatment ( OR = 17.24; 95% CI [12.89, 22.58]). This preference was present at any level of effectiveness (smallest worthwhile effect = 0%). Respondents preferred physical therapies with higher effectiveness, quicker symptom improvement, lower out-of-pocket expenses, reduced side effects, and shorter duration. Respondents were willing to pay up to A$355 per month for physical therapies over no treatment. Older and less-educated respondents had weaker preferences for physical therapies. CONCLUSION: Respondents had a strong preference for any recommended physical therapies over no treatment for low back pain, even when effects were very small. Clinicians should discuss likely effectiveness, time for improvement, side effects, and treatment duration when supporting patients to choose between recommended physical therapies. J Orthop Sports Phys Ther 2025;55(9):602-610. Epub 30 July 2025. doi:10.2519/jospt.2025.13409
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".