Optimizing Paediatric Paracetamol Dosing: Transitioning from Age-Based to Automated Weight-Based Calculations in Saudi Arabia
Bibliographic record
Abstract
Introduction: Paracetamol is one of the most commonly used over-the-counter (OTC) medications for managing conditions in paediatric patients. Objectives: Evaluate paediatric paracetamol dosing by age versus weight, and propose automated solutions for OTC dosing. Methods: A retrospective study compared weight-based (15mg/kg) to age-based dosing in children 0-13years of age. Results: A total of 352 paediatric patients were included, with a mean age of 5years, 4.8 months (SD=48 months) and a median weight of 16.9 kg (IQR:10.4 to 25.75).We observed that 71.59% of patients (n=252) are likely to be underdosed when using age-specific dosages rather than their actual weight. Conversely, 27.56% (n=97) could be overdosed, and only 0.85% (n=3) received an accurate dose. The discrepancies between the age-based and weight-based doses ranged from 1 mg to 928.5 mg per dose. We noted a correlation between the patient's weight and the variation in dose between the two different methods of dosing; r(350)=0.82, p<0.001. The regression was significant [F(1,350)=742.18, p<0.001], with 67.2% of the variability in dose difference explained by the patient's weight. The average difference in dose was 6.9 mg for every kilogram of the patient's weight. A linear regression analysis revealed that the patient's age was also a significant predictor of dose difference (F(1,350)=150.12, p<0.0001), with age explaining 29.8% of the variance. On average, the dose of paracetamol differed by 1.5 mg for each additional month of patient age (p<0.001). Expected impact: To optimise the dosing of OTC medication and enhance safety, we propose the use of supervised machine learning with Saudi growth charts, and the integration of a weight-based dosing calculator into the ‘Sehaty’ app.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".