Consumer Trends Reflected in the Contents of the Pediatric Esophagus: A 20‐Year Review
Bibliographic record
Abstract
Objectives To evaluate epidemiological trends of pediatric esophageal foreign body (EFB) ingestion over two decades. Methods A retrospective analysis was performed using data from the National Electronic Injury Surveillance System (NEISS) database for children <18 years who presented to a United States Emergency Department (ED) with EFB between 2003 and 2022. Number of cases and type of EFB were recorded. Rates of EFBs over time were analyzed via linear regression. Results A total of 52,315 EFB cases were identified over the 20‐year period, with a national estimate of 1,589,325 cases. The most frequently ingested objects were coins (37.6%), toys (13.5%), and batteries (6.8%). Overall incidence of EFB ingestion increased from 7.3 to 14.2/10,000 children from 2003 to 2022 (R2 = 0.8, p < 0.0001). Incidence of coin ingestion increased from 3 to 4.5/10,000 children (R2 = 0.06, p = 0.335) but represented a smaller proportion of all EFB over time (66% in 2003 versus 43% in 2022). Incidence of magnet, battery, and toy ingestion have increased from 0.3 to 1.0/10,000 (R2 = 0.9, p < 0.0001), 0.3 to 1/10,000 (R2 = 0.7, p < 0.0001), and 0.6 to 2.3/10,000 (R2 = 0.8, p < 0.0001) children, respectively, between 2003 and 2022. The proportion of magnet, battery, and toy ingestion have increased over time (3.2%, 6.5%, and 11.8%, respectively, in 2003 to 11.4%, 11.7%, and 22.2%, respectively, in 2022). Conclusion Magnet, battery, and toy ingestion have increased significantly in the past two decades, while the proportion of coin ingestion has decreased. This trend may reflect shifts within the consumer market and increased availability of electronics concurrent with the adoption of digital currency. Level of Evidence 4 Laryngoscope, 2024
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".