The Alberta Health Technology Assessment (HTA) Ambassador Program: The Development of a Contextually Relevant, Multidisciplinary Clinical Practice Guideline for Non-specific Low Back Pain: A Review
Bibliographic record
Abstract
PURPOSE: To describe the development of a contextually relevant multidisciplinary clinical practice guideline (CPG) for non-specific low back pain (LBP) and to discuss its value to the management of LBP and the practice of physiotherapy. METHOD: To mitigate an identified knowledge gap for Alberta primary-care practitioners in the management of non-specific LBP, a collaborative process was developed to engage multidisciplinary health care providers in designing a primary-care CPG for non-specific LBP. A comprehensive review of published LBP guidelines identified the seven highest-quality CPGs; these were used to inform a multidisciplinary guideline development group (GDG) as they developed the CPG. RESULTS: The GDG constructed a CPG for non-specific LBP along with point-of-care decision-support and patient-education tools. CONCLUSIONS: The Ambassador Program on Low Back Pain worked with front-line clinicians from across Alberta to review the best available evidence in developing a CPG responsive to the Alberta context. This CPG is intervention specific and provides a wide range of primary-care practitioners with the best available evidence to inform their clinical decisions in managing non-specific LBP.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".