New Ways of Detecting ADRs in Neonates and Children
Bibliographic record
Abstract
Severe adverse drug reactions (ADRs) cause 5-7% of all hospital admissions, an estimated 2,000,000 severe reactions, and over 100,000 deaths each year in the USA. A recent systematic review indicated that the overall incidence of ADRs was 11% in hospitalized children and 1% in outpatients. Detecting ADRs in neonates and children is challenging, particularly because there are fewer clinical trials involving children than adults and drug use in children is common without a labeled indication. Ontogeny and significant physiological changes related to age have an impact on metabolic drug clearance and pharmacodynamics in children compared to adults, as well as on drug action. A variety of strategies have been developed for the identification and further evaluation of ADRs, starting from case reports and advancing into more structured methodologies, such as active surveillance, for accumulating the necessary information. While each approach has merit, a comprehensive surveillance approach with different methods is required to monitor drug safety in the post-market period. Among the methods that have shown value in neonates and children are anecdotal reporting, voluntary organized reporting, prescription event monitoring, pharmacoepidemiology using administrative databases, and active surveillance. There is an urgent need to improve the evaluation of paediatric drug safety in the pre- and post-market phases of drug evaluation in order to better predict in whom serious harm may occur and better ensure the safe use of drugs for neonates and children.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".