Dosing of enteral acetaminophen in critically ill children: a cohort study
Bibliographic record
Abstract
OBJECTIVE: Acetaminophen is the most common medication prescribed in children's hospitals. The aim of the study was to estimate the frequency and risk factors for acetaminophen underdosing and overdosing in the paediatric intensive care unit (PICU). DESIGN: Retrospective cohort of drug administrations in a large tertiary care PICU. PATIENTS: All PICU admissions, less than 18 years of age, admitted between 1 January 2008 and 1 January 2018, having received at least one dose of enteral acetaminophen. METHODS: The primary outcome was acetaminophen underdosing and overdosing, defined as doses exceeding the 10% upper and lower limits of the standard reference range (10-15 mg/kg) and 10% above daily maximum dose (75 mg/kg). A generalised estimating equation regression assessed patient risk factors for single underdosing, single overdosing and cumulative daily overdosing of acetaminophen. RESULTS: Of the 147 485 doses of enteral acetaminophen administered, 7814 (5.3%) were single underdoses (1 in every 19 doses) and 4640 (3.1%) were single overdoses (1 in every 32 doses). There were 6813 cumulative overdose days (1 in every 9 patient-days). Risk factors for both underdosing and overdosing included older age and cardiac admission, whereas risk factors for cumulative overdosing were young age and cardiac admission. Electronic prescribing increased the risk of underdosing and overdosing, but decreased cumulative acetaminophen overdosing (relative risk 0.51, p=0.001). CONCLUSION: Acetaminophen underdosing and overdosing are common in the PICU and can be detected with pharmacoepidemiology. Electronic prescribing increased the risk of single underdosing and overdosing, although it reduced the risk of cumulative overdosing.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".