Multidisciplinary chronic pain management in a rural Canadian setting.
Bibliographic record
Abstract
INTRODUCTION: Chronic pain is prevalent, complex and most effectively treated by a multidisciplinary team, particularly if psychosocial issues are dominant. The limited access to and high costs of such services are often prohibitive for the rural patient. We describe the development and 18-month outcomes of a small multidisciplinary chronic pain management program run out of a physician's office in rural Alberta. METHODS: The multidisciplinary team consisted of a family physician, physiatrist, psychologist, physical therapist, kinesiologist, nurse and dietician. The allied health professionals were involved on a part-time basis. The team triaged referral information and patients underwent either a spine or medical care assessment. Based on the findings of the assessment, the team managed the care of patients using 1 of 4 methods: consultation only, interventional spine care, supervised medication management or full multidisciplinary management. We prospectively and serially recorded self-reported measures of pain and disability for the supervised medication management and full multidisciplinary components of the program. RESULTS: Patients achieved clinically and statistically significant improvements in pain and disability. CONCLUSION: Successful multidisciplinary chronic pain management services can be provided in a rural setting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".