Antidotes for childhood toxidromes
Bibliographic record
Abstract
BACKGROUND: Poisoning causes significant morbidity and sometimes mortality in children worldwide. The clinical skill of toxidrome recognition followed by the timely administration of an antidote specific for the poison is essential for the management of children with suspected poisoning. This is a narrative review on antidotes for toxidromes in paediatric practice. METHODS: A literature search was conducted on PubMed with the keywords "antidote", "poisoning", "intoxication", "children" and "pediatric". The search was customized by applying the appropriate filters (species: humans; age: birth to 18 years) to obtain the most relevant articles for this review article. RESULTS: Toxidrome recognition may offer a rapid guide to possible toxicology diagnosis such that the specific antidote can be administered in a timely manner. This article summarizes toxidromes and their respective antidotes in paediatric poisoning, with an emphasis on the symptomatology and source of exposure. The antidote and specific management for each toxidrome are discussed. Antidotes are only available for a limited number of poisons responsible for intoxication. Antidotes for common poisonings include N-acetyl cysteine for paracetamol and sodium thiosulphate for poisoning by cyanide. CONCLUSION: Poisoning is a common cause of paediatric injury. Physicians should be familiar with the recognition of common toxidromes and promptly use specific antidotes for the management of childhood toxidromes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".