Implementing Stratified Primary Care Management for Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Within-study cost-utility analysis. OBJECTIVE: To explore the cost-utility of implementing stratified care for low back pain (LBP) in general practice, compared with usual care, within risk-defined patient subgroups (that is, patients at low, medium, and high risk of persistent disabling pain). SUMMARY OF BACKGROUND DATA: Individual-level data collected alongside a prospective, sequential comparison of separate patient cohorts with 6-month follow-up. METHODS: Adopting a cost-utility framework, the base case analysis estimated the incremental LBP-related health care cost per additional quality-adjusted life year (QALY) by risk subgroup. QALYs were constructed from responses to the 3-level EQ-5D, a preference-based health-related quality of life instrument. Uncertainty was explored with cost-utility planes and acceptability curves. Sensitivity analyses examined alternative methodological approaches, including a complete case analysis, the incorporation of non-back pain-related health care use and estimation of societal costs relating to work absence. RESULTS: Stratified care was a dominant treatment strategy compared with usual care for patients at high risk, with mean health care cost savings of £124 and an incremental QALY estimate of 0.023. The likelihood that stratified care provides a cost-effective use of resources for patients at low and medium risk is no greater than 60% irrespective of a decision makers' willingness-to-pay for additional QALYs. Patients at medium and high risk of persistent disability in paid employment at 6-month follow-up reported, on average, 6 fewer days of LBP-related work absence in the stratified care cohort compared with usual care (associated societal cost savings per employed patient of £736 and £652, respectively). CONCLUSION: At the observed level of adherence to screening tool recommendations for matched treatments, stratified care for LBP is cost-effective for patients at high risk of persistent disabling LBP only. LEVEL OF EVIDENCE: 2.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".