Does active surveillance of serious and life-threatening adverse drug reactions improve reporting?
Bibliographic record
Abstract
Adverse drug reactions (ADRs) are an important cause of illness and death (1). Of particular concern is the alarming lack of ADR data in the paediatric population, which, therefore, limits the ability to avoid or prevent these occurrences. Only a minority of prescribed pharmaceuticals on the market in North America have been tested in paediatric populations, and most of them are used without the benefits of adequate guidelines for safety or efficacy (2). Postmarketing surveillance is, therefore, essential for early detection of ADRs, and relies mainly on voluntary reporting. A major criticism of current voluntary surveillance by health professionals has been the high level of under-reporting. Health-related accreditation bodies estimate that 95% of all ADRs are not reported (3). The Canadian Paediatric Surveillance Program (CPSP) launched a specific study to enhance reporting of serious and life-threatening ADRs in children, and has been collecting data since 2004. The first study objective is the identification of products most frequently causing ADRs in children, of the type of reactions encountered, and of any ADRs not currently captured by existing spontaneous reporting systems. Further objectives are quality data collecting, using ‘ADR Tips of the Month’ to build support and awareness of the study, facilitating case ascertainment, and impacting new information relating to the study or broader ADR surveillance topics. In 2008 and 2009, evaluations were conducted to collect more information on reporting practices of participants, and to assess the value of the CPSP surveillance methodology in supporting recognition/reporting of serious and life-threatening ADRs. Information was gathered on the ability of the CPSP to overcome documented barriers to reporting associated with passive surveillance, and the effectiveness of collaborative models in identifying solutions to improve ADR recognition and reporting. The current article presents study findings and results of the evaluative surveys.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".