Pediatric Exposures Reported to the Toxicology Investigators Consortium, 2010–2015
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Poisoning is the leading cause of injury death in pediatric patients. Hospital and provider readiness, including pharmacy stocking, depends on reliable surveillance data describing local patterns of age-specific clinically significant exposures and the therapeutic modalities employed in their treatment. We aimed to characterize trends in clinically significant toxic exposures and their management. METHODS: We performed a retrospective review of patients 18 years or younger in the American College of Medical Toxicology's Toxicology Investigators Consortium (ToxIC) Registry, a self-reporting database completed by bedside consulting medical toxicologists. We reviewed cases from January 1, 2010, through December 31, 2015. In 2015, ToxIC included 101 health care facilities. Data collected included demographics, geographic region, encounter and exposure details, survival, and therapeutic modalities employed, including antidotes, antivenoms, gastric decontamination, enhanced elimination, hyperbaric oxygen therapy, and extracorporeal membrane oxygenation. RESULTS: From 2010 to 2015, 11,616 consults were recorded in ToxIC. Pediatric consultations increased from 934 (23.7%) in 2010 to 2425 (29.9%) in 2015 (P < 0.001). Exposures were most commonly reported in females (57.8%) and adolescents (59.4%). Intentional ingestions (55.5%) comprised the majority of cases. The most frequent agents of exposure were analgesics (21.0%). There were 38 deaths reported (0.9%). The antidote used most commonly was N-acetylcysteine (11.0%). Geographic variation was demonstrated in prevalence of envenomations and heavy metal exposures, their respective treatments, and overall use of decontamination. CONCLUSIONS: Toxicology consultations for pediatric exposures increased from 2010 to 2015. Understanding which pediatric exposures require toxicologist management, the therapies most frequently employed, and geographical patterns is paramount to facility-level planning, pharmacy stocking, and provider education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".