Consensus for a primary care clinical decision‐making tool for assessing, diagnosing, and managing low back pain in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is a common condition causing disability and high healthcare costs. Alberta faces challenges with unnecessary referrals to specialists and long wait times. A province-wide standardized clinical care pathway based on evidence-based best practices can improve efficiency, reduce wait times, and enhance patient outcomes. Implementing such pathways has shown success in other areas of healthcare in Alberta. This study developed a clinical decision-making pathway to standardize care and minimize uncertainty in assessment, diagnosis, and management. METHODS: A systematic rapid review identified existing tools and evidence that could support a comprehensive LBP clinical decision-making tool. Forty-seven healthcare professionals participated in four rounds of a modified Delphi approach to reach consensus on the assessment, diagnosis, and management of patients presenting to primary care with LBP in Alberta, Canada. This project was a collaborative effort between Alberta Health Services' Bone and Joint Health Strategic Clinical Network (BJHSCN) and the Alberta Bone and Joint Health Institute (ABJHI). RESULTS: A province-wide expert panel consisting of professionals from different health disciplines and regions collaborated to develop an LBP clinical decision-making tool. This tool presents clinical care pathways for acute, subacute, and chronic LBP. It also provides guidance for history-taking, physical examination, patient education, and management. CONCLUSIONS: This clinical decision-making tool will help to standardize care, provide guidance on the diagnosis and management of LBP, and assist in clinical decision-making for primary care providers in both public and private sectors.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Other design | high |
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 | 0.105 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".