EQ-5D-5L and SF-6Dv2 utility scores in people living with chronic low back pain: a survey from Quebec
Bibliographic record
Abstract
OBJECTIVE: To describe how chronic low back pain (CLBP) impacts on utility scores and which patients' characteristics most affect these scores in the province of Quebec. SETTINGS: Province of Quebec, Canada. PARTICIPANTS: 569 adult patients with CLBP. METHODS AND OUTCOMES: An online survey on low back pain was conducted between October 2018 and January 2019. The EuroQol Five Dimensions (EQ-5D-5L) and the Short Form Six Dimensions version 2 (SF-6Dv2) are two generic preference-based measures used to evaluate health-related quality of life (HRQoL) and provide quality-adjusted life-year utility values. RESULTS: The number of subjects who agreed to participate was 610, but 41 were excluded because 8 had low back pain for less than 3 months and 33 did not start the survey. A total of 569 subjects were analysed, but only 410 completed the survey up to the EQ-5D-5L or SF-6Dv2 sections. Median (range) of EQ-5D-5L was 0.622 (-0.072 to 0.905), and mean (range) of SF-6Dv2 and EQ-Visual Analogue Scale was 0.561 (0.301-0.829) and 51.0 (0-100), respectively. In all multivariate models, health or life satisfaction increased the health utility score, while pain reduced it. Co-occurring health problems were present for a majority (68%) of participants, mainly fatigue/insomnia (57.4%), musculoskeletal disorder (56.2%) and mental disorder (44%). CONCLUSION: This study provided utility scores with EQ-5D-5L and SF-6Dv2 in patients with CLBP in Quebec, and results were similar to other studies conducted in different settings. These values were well below those reported in the Quebec general population and highlight the association between CLBP and HRQoL.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".