The Role of Physician Assistants in Ophthalmology: A Literature Review
Bibliographic record
Abstract
Introduction: Physician assistants (PAs) are trained medical professionals who work under the supervision of physicians to assess, diagnose, and treat patients across various specialties. While their role is well-established in many areas of medicine, the responsibilities of PAs within ophthalmology remain underexplored and unclear. Objective: This literature review examines the role of PAs in ophthalmology in North America. The objective is to identify and analyze primary research that explores their clinical responsibilities, integration into ophthalmic care teams, and the outcomes associated with their involvement. Methods: A structured search was conducted using PubMed and Scopus databases. Mesh terms and key words related to physician assistants and ophthalmology were used to identify relevant primary research articles, with inclusion and exclusion criteria. Results: Seven articles met the inclusion criteria and were reviewed. These studies focused on the practice of PAs in ophthalmology within the United States. Key findings centered around four main themes: the scope of PA roles, the integration into ophthalmic practice and the facilitations and limitations of PAs in practice. Conclusion: Preliminary evidence suggests that PAs in ophthalmology contribute positively to both patient care and physician support. However, current research is limited and primarily focuses on their role within the United States. Further studies are needed to better understand the impact, scope, and potential of PAs in ophthalmology - particularly to explore how their role could be expanded and integrated within the Canadian healthcare system.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".