The STarT Back stratified care model for nonspecific low back pain: a model-based evaluation of long-term cost-effectiveness
Bibliographic record
Abstract
ABSTRACT: The STarT Back approach comprises subgrouping patients with low back pain (LBP) according to the risk of persistent LBP-related disability, with appropriate matched treatments. In a 12-month clinical trial and implementation study, this stratified care approach was clinically and cost-effective compared with usual, nonstratified care. Despite the chronic nature of LBP and associated economic burden, model-based economic evaluations in LBP are rare and have shortcomings. This study therefore produces a de novo decision model of this stratified care approach for LBP management to estimate the long-term cost-effectiveness and address methodological concerns in LBP modelling. A cost-utility analysis from the National Health Service perspective compared stratified care with usual care in patients consulting in primary care with nonspecific LBP. A Markov state-transition model was constructed where patient prognosis over 10 years was dependent on physical function achieved at 12 months. Data from the clinical trial and implementation study provided short-term model parameters, with extrapolation using 2 cohort studies of usual care in LBP. Base-case results indicate this model of stratified care is cost-effective, delivering 0.14 additional quality-adjusted life years at a cost saving of £135.19 per patient over a time horizon of 10 years. Sensitivity analyses indicate the approach is likely to be cost-effective in all scenarios and cost saving in most. It is likely this stratified care model will help reduce unnecessary healthcare usage while improving the patient's quality of life. Although decision-analytic modelling is used in many conditions, its use has been underexplored in LBP, and this study also addresses associated methodological challenges.
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How this classification was reachedexpand
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".