Treatments for paracetamol poisoning
Bibliographic record
Abstract
A 24 year old woman is brought to the emergency department semi-conscious after a suspected overdose; empty packs of paracetamol (acetaminophen) and diazepam are found with her. She is also taking carbamazepine for seizures. Her paracetamol concentration at the time of admission is 100 mg/L (660 µmol/L); she probably ingested the pills four to eight hours earlier. Paracetamol poisoning can cause severe hepatotoxicity owing to a minor but highly reactive metabolite produced by cytochrome P450 enzymes. At therapeutic doses, the metabolite ( N -acetyl-p-benzoquinoneimine; NAPQI) is detoxified by glutathione. However, in paracetamol overdose, glutathione stores are depleted and hepatotoxicity ensues, starting about eight hours after the overdose and potentially leading to fulminant liver failure within a few days. The risk of hepatotoxicity is calculated from the blood concentration of paracetamol and hours since ingestion (fig 1⇓). If the concentration is above the line on the nomogram, treatment should be considered. The risk of toxicity without treatment is low until concentrations are substantially higher than this line. The nomogram is inaccurate if presentation is very late or the overdose was taken over several hours. If a measurement cannot be obtained within eight hours, treatment decisions cannot wait for laboratory results. Risk is then based on reported ingested dose (≥200 mg/kg or 10 g in Australia, >75 mg/kg or 4 g in United Kingdom) or on evidence of hepatotoxicity if the overdose was taken >24 hours ago. Fig 1 Nomograms for the treatment of paracetamol poisoning. Concentrations above the lines require treatment. Nomograms for clinical use usually show just one of the lines to avoid confusion. The US/Australia line is sometimes referred to as the Rumack-Matthew line and is commonly used in Canada, New Zealand, and parts of Europe and Asia Antidotes acetylcysteine and methionine provide a substrate for further glutathione synthesis, …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".