Experience with physician assistants in a Canadian arthroplasty program.
Bibliographic record
Abstract
BACKGROUND: Recent increases in orthopedic surgical services in Canada have added further demand to an already stretched orthopedic workforce. Various initiatives have been undertaken across Canada to meet this demand. One successful model has been the use of physician assistants (PAs) within the Winnipeg Regional Health Authority (WRHA). This study documents the effect of PAs working in an arthroplasty practice from the perspective of patients and health care providers. We also describe the costs, time savings for surgeons and the effects on surgical throughput and waiting times. METHODS: We calculated time savings by the use of a daily diary kept by the PAs. Surgeons', residents', nurses' and patients' opinions about PAs were recorded by use of a self administered questionnaire. We calculated costs using forgone general practitioner (GP) surgical assist fees and salary costs for PAs. We obtained information about surgical throughput and wait times from the WRHA waitlist database. RESULTS: In this study, PAs "saved" their supervising physician about 204 hours per year; this time can be used for other clinical, administrative or research duties. Physician assistants are regarded as important members of the health care team by surgeons, nurses, orthopedic residents and patients. When we compared the billing costs with those that would have been generated by the use of GP surgical assists, PAs were essentially cost neutral. Furthermore, they potentially freed GPs from the operating room to spend more time delivering primary care. We found that use of the double operating room model facilitated by PAs increased the surgical throughput of primary hip and knee replacements by 42%, and median wait times decreased from 44 weeks to 30 weeks compared with the preceding year. CONCLUSION: Physician assistants integrate well into the care team and can increase surgical volumes to reduce wait times in a cost-effective manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".