Why pediatric patients with cancer visit the emergency department: United States, 2006–2010
Bibliographic record
Abstract
BACKGROUND: Little is known about emergency department (ED) use among pediatric patients with cancer. We explored reasons prompting ED visits and factors associated with hospital admission. PROCEDURE: A retrospective cohort analysis of pediatric ED visits from 2006 to 2010 using the Nationwide Emergency Department Sample, the largest all-payer database of United States ED visits. Pediatric patients with cancer (ages ≤19 years) were identified using Clinical Classification Software. Proportion of visits and disposition for the top ten-ranking non-cancer diagnoses were determined. Weighted multivariate logistic regression was performed to analyze factors associated with admission versus discharge. RESULTS: There were 294,289 ED visits by pediatric patients with cancer in the U.S. over the study period. Fever and fever with neutropenia (FN) were the two most common diagnoses, accounting for almost 20% of visits. Forty-four percent of pediatric patients with cancer were admitted to the same hospital, with admission rates up to 82% for FN. Risk factors for admission were: FN (odds ratio (OR) 8.58; 95% confidence interval (CI) 5.97-12.34); neutropenia alone (OR 7.28; 95% CI 5.08-10.43), ages 0-4 years compared with 15-19 years (OR 1.19; 95% CI 1.08-1.31) and highest median household income ZIP code (OR 1.27; 95% CI 1.08-1.49) compared with lowest. "Self-pay" visits had lower odds of admission (OR 0.42; 95% CI 0.35-0.51) compared with public payer. CONCLUSION: FN was the most common reason for ED visits among pediatric patients with cancer and is the condition most strongly associated with admission. Socioeconomic factors appear to influence ED disposition for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".