15 Availability of neonatal specific data in labelling of commonly used anti-infective drugs: comparison among three regulatory agencies
Bibliographic record
Abstract
Introduction Neonatal-specific drug information is critical for optimal pharmacotherapeutic management of neonates in the Neonatal Intensive Care Unit (NICU). To date, the extent of the availability of neonatal-specific drug information for even the most commonly used drugs in the NICU has not been established. Our objective was to examine and compare the quantity and quality of the available neonatal-specific data in the most updated prescription labelling of anti-infective drugs used in the NICU among the United States Food and Drug Administration (FDA), United Kingdom Medicines and Healthcare products Regulatory Agency (MHRA) and Health Canada (HC).Methodology We identified updated Standard Product Labels (FDA), Product Monographs (HC) and Summaries of Product Characteristics (MHRA) of 31 anti-infective drugs listed among the most common 100 prescriptions in the NICU. We reviewed data regarding approval in neonates, availability and type of neonatal-specific studies, and the presence of neonatal-specific adverse events and warnings. We defined approval as having either a specific indication or dosing information for the specified population.Results We excluded eight drugs due to a lack of access to their most updated labelling. Of the 23 drugs included in the analysis, the FDA approved 13 (57%) and 10 (43%) drugs for term and preterm neonates, respectively, compared to 14 (61%) and seven (30%) by the MHRA and nine (39%) and four (17%) by Health Canada. Term-neonatal specific information was presented for 15 (65%), 18 (78%), and 10 (43%) drugs in FDA, MHRA, and HC labelling, while preterm-neonatal specific information was available for 11 (48%), 10 (43%), and four (17%) drugs, respectively. Six drugs had no neonatal-specific information; nine drugs had no preterm-specific information.Conclusion The global lack of neonatal-specific information for the most frequently used drugs in the NICU poses critical challenges for neonatal care. Health Canada presents the most challenging drug approval agency lagging behind the other jurisdictions in the provision of important neonatal data. There is an emergent need for regulatory mechanisms to ensure the inclusion of existing pediatric data in Canadian drug monographs.
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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 | 0.037 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".