Novel strategy to personalise use of ibuprofen for closure of patent ductus arteriosus in preterm neonates
Bibliographic record
Abstract
Objective Exploration of a novel therapeutic drug monitoring (TDM) strategy to personalise use of ibuprofen for closure of patent ductus arteriosus (PDA) in preterm neonates. Design Prospective, single-centre, open-label, pharmacokinetics study in preterm neonates. Setting Neonatal intensive care unit at McMaster Children’s Hospital. Patients Neonates with a gestational age ≤28+6 weeks treated with oral ibuprofen for closure of a PDA. Methods Population pharmacokinetic parameters, concentration-time profiles and exposure metrics were obtained using pharmacometric modelling and simulation. Main outcome measure Association between ibuprofen plasma concentrations measured at various sampling time points on the first day of treatment and attainment of the target exposure over the first 3 days of treatment (AUC0–72h >900 mg·hour/L). Results Twenty-three preterm neonates (median birth weight 780 g and gestational age 25.9 weeks) were included, yielding 155 plasma ibuprofen plasma samples. Starting from 8 hours’ postdose on the first day, a strong correlation between ibuprofen concentrations and AUC0–72h was observed. At 8 hours after the first dose, an ibuprofen concentration >20.5 mg/L was associated with a 90% probability of reaching the target exposure. Conclusion We designed a novel and practical TDM strategy and have shown that the chance of reaching the target exposure (AUC0–72h >900 mg·hour/L) can be predicted with a single sample collection on the first day of treatment. This newly acquired knowledge can be leveraged to personalise ibuprofen dosing regimens and improve the efficacy of ibuprofen use for pharmacological closure of a PDA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".