Six-month outcomes of an integrated primary care prevention and management program for chronic low back pain (LBP)
Bibliographic record
Abstract
Abstract Background Accessible interdisciplinary, patient-centered and integrated primary care programs for chronic pain are needed for averting chronicity and improving patient outcomes. We sought 1) to develop an interdisciplinary low back pain (LBP) primary care program, and 2) to evaluate the program’s implementation and impact on patients’ physical and mental health over 6 months. A quasi-experimental pre-test/post-test design was used, and participants completed baseline, 3 and 6 months post-intervention evaluations. Referral and program process indicators were measured. Methods Clinically meaningful change in patient reported outcome measures of pain interference and intensity, physical function, depression and anxiety were defined a priori and evaluated. A multilevel regression analysis was performed to evaluate the impact of the program on change in individuals’ pain interference over 6 months. Results Forty six percent of participants were seen within 60 days of referral to the LBP program, and 464 individuals completed the program. The majority (≥ 60%) experienced a clinically meaningful improvement for pain intensity and interference at six months. A greater proportion of those with moderate (71%) or high risk (81%) of chronicity showed an improvement in pain interference than those with low risk (51%). A similar pattern emerged for depression and self-efficacy, but not for anxiety. Significant predictors of improvements in pain interference included higher prognostic risk of chronicity, younger age, sex, and lower baseline disability. Conclusion Results supported an improvement in participants’ confidence to manage their LBP. Future research will compare alternative modes of delivery (e.g. telehealth) and stratification approaches to further tailor resources to individuals’ needs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".