Consensus for a primary care clinical decision-making tool for assessing, diagnosing, and managing shoulder pain in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Shoulder pain is a highly prevalent condition and a significant cause of morbidity and functional disability. Current data suggests that many patients presenting with shoulder pain at the primary care level are not receiving high quality care. Primary care decision-making is complex and has the potential to influence the quality of care provided and patient outcomes. The aim of this study was to develop a clinical decision-making tool that standardizes care and minimizes uncertainty in assessment, diagnosis, and management. METHODS: First a rapid review was conducted to identify existing tools and evidence that could support a comprehensive clinical decision-making tool for shoulder pain. Secondly, provincial consensus was established for the assessment, diagnosis, and management of patients presenting to primary care with shoulder pain in Alberta, Canada using a three-step modified Delphi approach. This project was a highly collaborative effort between Alberta Health Services' Bone and Joint Health Strategic Clinical Network (BJH SCN) and the Alberta Bone and Joint Health Institute (ABJHI). RESULTS: A clinical decision-making tool for shoulder pain was developed and reached consensus by a province-wide expert panel representing various health disciplines and geographical regions. This tool consists of a clinical examination algorithm for assessing, diagnosis, and managing shoulder pain; recommendations for history-taking and identification of red flags or additional concerns; recommendations for physical examination and neurological screening; recommendations for the differential diagnosis; and care pathways for managing patients presenting with rotator cuff disease, biceps pathology, superior labral tear, adhesive capsulitis, osteoarthritis, and instability. CONCLUSIONS: This clinical decision-making tool will help to standardize care, provide guidance on the diagnosis and management of shoulder pain, 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
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 | 0.117 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".