Acetaminophen psi parameter: A useful tool to quantify hepatotoxicity risk in acute acetaminophen overdose
Bibliographic record
Abstract
CONTEXT: The risk of hepatotoxicity secondary to acute acetaminophen overdose is related to serum acetaminophen concentration and lag time from ingestion to N-acetylcysteine (NAC) therapy. Psi (Greek letter ψ) is a toxicokinetic parameter that takes the acetaminophen level at 4 h post-ingestion ([APAP](4 h)) and the time-to-initiation of NAC (tNAC) into account and was found to be significantly predictive of hepatotoxicity in Canadian patients with acetaminophen overdose treated with intravenous NAC. OBJECTIVE: We report the relationship of psi and hepatotoxicity in a Thai population with acute acetaminophen overdose. METHODS: This is a retrospective study of patients with acute paracetamol overdose during January 2004 to June 2009 at Siriraj Hospital. Patients were treated with the standard 21-h intravenous NAC regimen. Univariate analyses were performed with logistic regression to assess the relationships of psi, [APAP](4 h), and tNAC, and hepatotoxicity. RESULTS: A total of 127 patients were enrolled. The median (interquartile range; IQR) of [APAP](4 h) was 267.8 (196.0-380.0) mg/L. The median (IQR) of tNAC was 8.5 (6.2-12.0) h. Thirteen patients (10.2%) developed hepatotoxicity. Univariate analysis revealed [APAP](4 h), tNAC, and psi as statistically significant predictors of hepatotoxicity. DISCUSSION AND CONCLUSION: The psi parameter is a reliable prognostic tool to predict hepatotoxicity secondary to acute acetaminophen overdose treated with intravenous NAC. Our evidence shows that psi may be a more superior tool than either acetaminophen level or time-to-initiation of NAC at predicting hepatotoxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".