Multiplying the serum aminotransferase by the acetaminophen concentration to predict toxicity following overdose
Bibliographic record
Abstract
CONTEXT: The first available predictors of hepatic injury following acetaminophen (APAP) overdose are the serum APAP and aminotransferases [AT, i.e., aspartate (AST) aminotransferase or alanine (ALT) aminotransferase]. OBJECTIVE: We describe the initial value, rate of change, and interrelationship between these biomarkers in patients who develop hepatotoxicity despite treatment following acute overdose. A new parameter, the APAP × AT multiplication product, is proposed for early risk stratification. METHODS: We conducted a descriptive study of individuals selected from a multicenter retrospective cohort of patients hospitalized for APAP poisoning. We selected those acute APAP overdose patients who subsequently developed AT > 1,000 IU/L. Rising serum AT values were compared to simultaneously measured (or estimated) falling serum APAP. The APAP × AT was expressed relative to initiation of acetylcysteine therapy and grouped by time to meeting hepatotoxicity criteria. RESULTS: In the 94 cases studied, serum APAP concentrations were still appreciable [median 570 (interquartile range (IQR) 314-983) μmol/L] at the time of the first measured AT [211 (77-511) IU/L at 15.3 (12.1-19.2) h post-ingestion], yielding an initial APAP × AT of 99,000 (52,000-240,000) μmol × IU/L(2). Because serum AT rose rapidly (doubling time 9.5 h ) and APAP fell slowly (half-life 4.8 h), the multiplication product remained elevated during the first 12-24 h of antidotal therapy, especially among patients who developed earlier hepatotoxicity (AT > 1,000 IU/L). DISCUSSION AND CONCLUSIONS: The APAP × AT multiplication product, calculated at the time of presentation and after several h of antidotal therapy, holds promise as a new risk predictor following APAP overdose. It requires neither graphical interpretation nor accurate time of ingestion, two limitations to current risk stratification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".