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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".