Forecasting the effect of physician assistants in a pediatric ED
Bibliographic record
Abstract
BACKGROUND: Most pediatric ED visits are for nonemergent problems. Physician assistants are well trained to manage these patients; however, their effect on patient flow in a pediatric ED is unknown. OBJECTIVES: To compare the effect on key pediatric ED efficiency indicators of extending physician coverage versus adding PAs with equivalent incremental costs. METHODS: We used discrete event simulation modeling to compare the effect of additional physician coverage versus adding PAs on wait time, length of stay (LOS), and patients leaving without being seen. RESULTS: Simulation of extended physician coverage reduced wait times, LOS, and rates of leaving without being seen across acuity levels. Adding PAs reduced wait times and LOS for high-acuity visits, and slightly increased the LOS for low-acuity visits. CONCLUSIONS: With restricted autonomy, PAs mainly benefitted the high-acuity patients. Increasing the level of PA autonomy was critical in broadening the effect of PAs to all acuity levels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".