Paediatric adverse drug reaction reporting: understanding and future directions.
Bibliographic record
Abstract
BACKGROUND: Severe adverse drug reactions (ADRs) are an important cause of childhood morbidity and mortality. 95% of ADRs are likely not reported, less than 25% of marketed drugs can be advertised as safe and effective in children; yet over 50% of Canadian children receive prescription drugs annually. OBJECTIVES: To increase understanding of reported ADRs in Canadian children. METHODS: A retrospective analysis of 1193 suspected ADRs reported to Health Canada (January 1998 - May 2002). These data were a paediatric subset of the Canadian Adverse Drug Reaction Information System database. RESULTS: 58.6% of ADRs were for children over 13 years. 61% of reports were defined by Health Canada as serious. Case outcomes include: death (n=41) and recovered with sequelae (n=14). 4 reports of interacting drugs had fatal outcomes. Drugs most frequently cited include: isotretinoin (n=56), paroxetine (n=42), methylphenidate (n=41), amoxicillin (n=40), and valproic acid (n=32). Most frequent reaction descriptors include: psychiatric disorders (isotretinoin and paroxetine) and nervous system disorders (valproic acid, bupropion and carbamazepine). Causal links between suspected ADRs and clinical outcomes have not been established. CONCLUSIONS: Current ADR reporting is insufficient to improve patient safety. More detailed reporting, including case outcomes, is needed. Mandatory ADR reporting is unlikely to improve underreporting. Trained surveillance personnel located in major health centres and solely dedicated to ADR reporting may provide a more accurate determination of ADRs in Canadian 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 | 0.081 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".