<p>A discrete choice experiment on preferences of patients with low back pain about non-surgical treatments: identification, refinement and selection of attributes and levels</p>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: Hospital-based health technology assessment (HB-HTA) needs to consider all relevant data to help decision making, including patients’ preferences. In this study, we comprehensively describe the process of identification, refinement and selection of attributes and levels for a discrete choice experiment (DCE). Methods: A mixed-methods design was used to identify attributes and levels explaining low back pain (LBP) patients’ choice for a non-surgical treatment. This design combined a systematic literature review with a patients’ focus group, one-on-one interactions with experts and patients, and discussions with stakeholder committee members. Following the patient’s focus group, preference exercises were conducted. A consensus about the attributes and levels was researched during discussions with committee members. Results: The literature review yielded 40 attributes to consider in patients’ treatment choice. During the focus group, one additional attribute emerged. The preference exercises allowed selecting eight attributes for the DCE. These eight attributes and their levels were discussed and validated by the committee members who helped reframe two levels in one of the attributes and delete one attribute. The final seven attributes were: treatment modality, pain reduction, onset of treatment efficacy, duration of efficacy, difficulty in daily living activities, sleep problem, and knowledge about their body and pain. Conclusion: This study is one of the few to comprehensively describe the selection process of attributes and levels for a DCE. This may help ensure transparency and judge the quality of the decision-making process. In the context of a HB-HTA unit, this strengthens the legitimacy to perform a DCE to better inform decision makers in a patient-centered care approach. Keywords: low back pain, preference, treatment, choice
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 it