Utilizing the physician assistant role: case study in an upper-extremity orthopedic surgical program
Bibliographic record
Abstract
BACKGROUND: Shortages with resources and inefficiencies with orthopedic services in Canada create opportunities for alternative staffing models and ways to use existing resources. Physician assistants (PAs) are a common provider used in specialty orthopedic services in the United States; however, Canada has limited experience with PAs. As part of a larger demonstration project, Alberta Health Services (AHS) implemented 1 PA position in an upper-extremity surgical program in Alberta, Canada, to demonstrate the role in 4 areas: preoperative, operative, postoperative and follow-up care. METHODS: = 47), and 2 years of clinic data on new patients. Data from a double operating room experiment detailed expected versus actual times for 3 phases of surgery (pre, during, post). RESULTS: Preoperatively, the PA prioritizes patient referrals for surgery and redirects patients to alternative care. In the second year with the PA in place, there was an increase in total new patients seen (113%). Postoperatively, the PA attended rounds on 5 surgeons' patients and handled follow-up care activities. Health care providers and patients reported that the PA provided excellent care. Findings from the operating room showed that the preparation time was greater than expected (38.6%), whereas the surgeon time (20.6%) and postsurgery time (37.2%) was less than expected. CONCLUSION: After 24 months the PA has become a valuable member of the health care team and works across the continuum of orthopedic care. The PA delivers quality care and improves system efficiencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".