Creating a multidisciplinary low back pain guideline: anatomy of a guideline adaptation process
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: A collaborative, multidisciplinary guideline adaptation process was developed to construct a single overarching, evidence-based clinical practice guideline (CPG) for all primary care practitioners responsible for the management of low back pain (LBP) to curb the use of ineffective treatments and improve patient outcomes. METHODS: The adaptation strategy, which involved multiple committees and partnerships, leveraged existing knowledge transfer connections to recruit guideline development group (GDG) members and ensure that all stakeholders had a voice in the guideline development process. Videoconferencing was used to coordinate the large, geographically dispersed GDG. Information services and health technology assessment experts were used throughout the process to lighten the GDG's workload. RESULTS: The GDG reviewed seven seed guidelines and drafted an Alberta-specific guideline during 10 half-day meetings over a 12-month period. The use of ad hoc subcommittees to resolve uncertainties or disagreements regarding evidence interpretation expedited the process. Challenges were encountered in dealing with subjectivity, guideline appraisal tools, evidence source limitations and inconsistencies, and the lack of sophisticated evidence analysis inherent in guideline adaptation. Strategies for overcoming these difficulties are discussed. CONCLUSION: Guideline adaptation is useful when resources are limited and good-quality seed CPGs exist. The Ambassador Program successfully utilized existing stakeholder interest to create an overarching guideline that aligned guidance for LBP management across multiple primary care disciplines. Unforeseen challenges in guideline adaptation can be overcome with credible seed guidelines, a consistently applied and transparent methodology, and clear documentation of the subjective contextualization process. Multidisciplinary stakeholder input and an open, trusting relationship among all contributors will ensure that the end product is clinically meaningful.
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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 | 0.104 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".