Exploring the cost–utility of stratified primary care management for low back pain compared with current best practice within risk-defined subgroups
Bibliographic record
Abstract
OBJECTIVES: Stratified management for low back pain according to patients' prognosis and matched care pathways has been shown to be an effective treatment approach in primary care. The aim of this within-trial study was to determine the economic implications of providing such an intervention, compared with non-stratified current best practice, within specific risk-defined subgroups (low-risk, medium-risk and high-risk). METHODS: Within a cost-utility framework, the base-case analysis estimated the incremental healthcare cost per additional quality-adjusted life year (QALY), using the EQ-5D to generate QALYs, for each risk-defined subgroup. Uncertainty was explored with cost-utility planes and acceptability curves. Sensitivity analyses were performed to consider alternative costing methodologies, including the assessment of societal loss relating to work absence and the incorporation of generic (ie, non-back pain) healthcare utilisation. RESULTS: The stratified management approach was a cost-effective treatment strategy compared with current best practice within each risk-defined subgroup, exhibiting dominance (greater benefit and lower costs) for medium-risk patients and acceptable incremental cost to utility ratios for low-risk and high-risk patients. The likelihood that stratified care provides a cost-effective use of resources exceeds 90% at willingness-to-pay thresholds of £4000 (≈ 4500; $6500) per additional QALY for the medium-risk and high-risk groups. Patients receiving stratified care also reported fewer back pain-related days off work in all three subgroups. CONCLUSIONS: Compared with current best practice, stratified primary care management for low back pain provides a highly cost-effective use of resources across all risk-defined subgroups.
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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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".