Preferences of patients with chronic low back pain about nonsurgical treatments: Results of a discrete choice experiment
Bibliographic record
Abstract
INTRODUCTION: This study aimed to assess patients' preferences of nonsurgical treatments for chronic low back pain (CLBP). METHOD: We conducted a discrete choice experiment (DCE) in Quebec, Canada, in 2018. Seven attributes were included: treatment modality, pain reduction, the onset of treatment efficacy, duration effectiveness, difficulties with daily activities, sleep problems, and knowledge of the patient's body and pain location. Treatment modalities were corticosteroid injections, supervised body-mind physical activities, supervised sports physical activities, physical manipulations, self-management courses, and psychotherapy. Utility levels were estimated using a logit model, a latent class model and a Bayesian hierarchical model. RESULTS: individuals. According to the Bayesian hierarchical model, the conditional relative importance weights of attributes were as follows: (1) treatment modality (34.79%), (2) pain reduction (18.73%), (3) difficulties with daily activities (11.71%), (4) duration effectiveness (10.06%), (5) sleep problems (10.05%), (6) onset of treatment efficacy (8.60%) and (7) knowledge of the patient's body and pain location (6.06%). According to the latent class model that found six classes of respondents with different behaviours (using Akaike and Bayesian criteria), the treatment modality was the most important attribute for all classes, except for class 4 for which pain reduction was the most important. In addition, classes 2 and 5 refused corticosteroid injections, while psychotherapy was preferred only in class 3. CONCLUSION: Given the preference heterogeneity found in the analysis, it is important that patient preferences are discussed and considered by the physicians. This will help to improve the patient care pathway in a context of a patient-centred model for a disease with growing prevalence. PATIENT OR PUBLIC CONTRIBUTION: A small group of patients was involved in the conception, design and interpretation of data. Participants in the DCE were all CLBP patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".