Exploring the Business Case for Improving Quality of Care for Patients With Chronic Rotator Cuff Tears
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Currently, management of patients presenting with chronic rotator cuff tears in Alberta is in need of quality improvements. This article explores the potential impact of a proposed care pathway whereby all patients presenting with chronic rotator cuff tears in Alberta would adopt an early, conservative management plan as the first stage of care; ultrasound investigation would be the preferred tool for diagnosing a rotator cuff tear; and only patients are referred for surgery once conservative measures have been exhausted. METHODS: We evaluate evidence in support of surgery and conservative management, compare care in the current state with the proposed care pathway, and identify potential solutions in moving toward optimal care. RESULTS: A literature search resulted in an absence of indications for either surgical or conservative management. Conservative management has the potential to reduce utilization of public health care resources and may be preferable to surgery. The proposed care pathway has the potential to avoid nearly Can $87 000 in public health care costs in the current system for every 100 patients treated successfully with conservative management. CONCLUSION: The proposed care pathway is a low-cost, first-stage treatment that is cost-effective and has the potential to reduce unnecessary, costly surgical procedures.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".