Paediatric poisoning exposures in schools: reports to Australia’s largest poisons centre
Bibliographic record
Abstract
OBJECTIVE: To describe poisoning exposures occurring at school in a large sample of Australian children. DESIGN: A population-based retrospective cohort study. SETTING: Cases reported to the New South Wales Poisons Information Centre (NSWPIC), Australia's largest poisons information centre, taking 50% of the nation's poisoning calls. PATIENTS: Poisoning exposures occurring in children and adolescents while at school were included, over a 4.5-year period (January 2014 to June 2018). MAIN OUTCOME MEASURES: Time trends in poisonings, demographics, exposure characteristics, substances involved, disposition. RESULTS: There were 1751 calls relating to exposures at school made to NSWPIC. Most calls concerned accidental exposures (60.8%, n=1064), followed by deliberate self-poisonings (self-harm, 12.3%, n=216). Over a quarter of cases were hospitalised (n=468), where the call originated from hospital or patients referred to hospital by NSWPIC. Disposition varied by exposure type, and hospitalisation was highest with deliberate self-poisonings (92.6%, n=200), recreational exposures (57.1%, n=12) and other intentional exposures (32.6%, n=45). The median age was 12 (IQR 8-15 years), and 54.7% were male (n=958). The most common pharmaceutical exposures were to paracetamol (n=100), methylphenidate (n=78) and ibuprofen (n=53), with the majority being deliberate self-poisonings. Copper sulfate was responsible for 55 science class cases, 45% of which were hospitalised. Cases may be increasing, with 81.3 (±8.2) calls per quarter, 2014-2016, and 129.3 (±24.3) calls per quarter, 2017-2018. CONCLUSIONS: Poisoning exposures occurring at school are common, with disposition and substances involved varying considerably by exposure reason. The relatively high number of referrals to hospital highlights the need for investigation into preventative measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".