Impact of Physicians’ Characteristics on the Admission Risk Among Children Visiting a Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess the impact of physicians' gender, work experience, and training on hospitalization among children visiting a pediatric emergency department (ED). METHODS: This retrospective cohort study used the computerized database of a tertiary care pediatric ED staffed by pediatric emergency physicians, general pediatricians, and general emergency physicians. Participants were all children evaluated in the ED between April 1, 2008, and March 31, 2009. The primary outcome was hospitalization, and secondary outcome was unscheduled return in the 48 hours after discharge from the ED. Determinants of outcomes were physician's gender, experience, and specialty training. Multivariate logistic regression was used to evaluate associations between physicians' characteristics and the risk of admission, adjusting for referral status, triage level, chief complaints, and other potential risk factors. RESULTS: Forty-five physicians evaluated 49,146 patients during the study period. Physicians' individual admission and return rates varied from 1% to 24% and 0% to 11%, respectively. On multiple logistic regression, physician's gender was not a predictor of admission but the physician's years of experience was slightly associated with both admission rates and unscheduled return visits. As a group, pediatric emergency physicians demonstrated a lower admission rate than physicians trained in general pediatric or general emergency medicine. CONCLUSIONS: Individual physician's admissions proportions vary widely. Providers' experience and specialization in pediatric emergency medicine are weak predictors of admission, whereas gender was not associated.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".