The Role of Physician Assistants in a Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to outline the clinical conditions presenting to a Canadian pediatric emergency department (ED), survey the opinions of physician assistants (PAs) and emergency pediatricians to determine which conditions they consider could be managed by PAs, and to estimate the proportion of the total pediatric ED volume that PAs could potentially manage. METHODS: We reviewed the 2007 British Columbia Children's Hospital database of ED visits to identify clinical presentation and chief complaint of all patients seen. International Classification of Diseases, 10th Revision codes were used to categorize the presenting complaint of each ED visit. Following categorization, the results were discussed by a focus group composed of pediatric emergency medicine-trained physicians and PAs, to review the list of chief complaints. We then surveyed via e-mailed questionnaire a group of emergency pediatricians (n = 17) and PAs (n = 5) to seek their opinion on the categories of clinical presentation appropriate for PA management. RESULTS: Of 38,722 visits, 9.2% were triaged as "resuscitation" or "emergent." We sorted the remaining 35,077 ED visits into 57 clinical categories. More than 85% of respondents selected 30 clinical categories for PA management with physician supervision, representing 74% of the total ED volume. Of these, 3 were also deemed appropriate for PA management without direct physician supervision. There were statistically significant differences in mean length of stay, waiting time, and admission rates between the clinical conditions selected for PA involvement. However, the difference in waiting time was not clinically meaningful. CONCLUSIONS: A large proportion of pediatric ED visits are designated as semiurgent or nonurgent. Clinicians (ED physicians and PAs) thought that the majority of these ED visits could be managed by PAs.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".