Predicting SF-6Dv2 utility scores for chronic low back pain using the Oswestry Disability Index and Roland-Morris Disability Questionnaire
Bibliographic record
Abstract
Background: Generic preference-based measures are used to evaluate disability and health-related quality of life (HRQoL).Objective: To evaluate if Short Form Six-Dimensions (SF-6Dv2) is correlated with specific current questionnaires used in chronic low back pain (CLBP) and if a predictive equation of SF-6Dv2 could be established.Methods: Between October 2018 and January 2019, an online survey on CLBP was conducted. HRQoL was measured with two specific questionnaires, i.e. Oswestry Disability Index (ODI) and Roland-Morris Disability Questionnaire (RMDQ), and with the new version of the SF-6Dv2 as a generic preference-based measure.Results: 402 subjects completed at least two of the three HRQoL questionnaires. Mean (95% confidence interval) of SF-6Dv2, ODI, or RMDQ were, respectively, 0.561 (0.553–0.569), 43.7 (42.1–45.2), and 10.3 (9.8–10.8). SF-6Dv2 was moderately correlated with ODI and RMDQ (r = −0.635 and r = −0.542, p < 0.001). The best model to predict SF-6Dv2 explained 50.6% of variability and included ODI. The correlation between actual and predicted SF-6Dv2 was 0.71.Conclusion: This study demonstrated that SF-6Dv2 was moderately correlated with ODI and RMDQ and that ODI was a better predictor. There was a strong correlation between actual and predicted SF-6Dv2 from multivariate models. These results suggest that the model can be used in similar studies to estimate the SF-6Dv2 when it was not measured.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".