Implementation of an integrated primary care prevention and management program for chronic low back pain (LBP): patient-reported outcomes and predictors of pain interference after six months
Bibliographic record
Abstract
BACKGROUND: Integrated primary care programs for patients living with chronic pain which are accessible, interdisciplinary, and patient-centered are needed for preventing chronicity and improving outcomes. Evaluation of the implementation and impact of such programs supports further development of primary care chronic pain management. This study examined patient-reported outcomes among individuals with low back pain (LBP) receiving care in a novel interdisciplinary primary care program. METHODS: Patients were referred by primary care physicians in four regions of Quebec, Canada, and eligible patients received an evidence-based interdisciplinary pain management program over a six-month period. Patients were screened for risk of chronicity. Patient-reported outcome measures of pain interference and intensity, physical function, depression, and anxiety were evaluated at regular intervals over the six-month follow-up. A multilevel regression analysis was performed to evaluate the association between patient characteristics at baseline, including risk of chronicity, and change in pain outcomes. RESULTS: Four hundred and sixty-four individuals (mean age 55.4y, 63% female) completed the program. The majority (≥ 60%) experienced a clinically meaningful improvement in pain intensity and interference at six months. Patients with moderate (71%) or high risk (81%) of chronicity showed greater improvement in pain interference than those with low risk (51%). Significant predictors of improvement in pain interference included a higher risk of chronicity, younger age, female sex, and lower baseline disability. CONCLUSION: The outcomes of this novel LBP program will inform wider implementation considerations by identifying key components for further effectiveness, sustainability, and scale-up of the program.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".