Prescription medicine use by one million Canadian children
Bibliographic record
Abstract
Drugs are an increasing and integral part of the treatment of childhood conditions. There is widespread concern regarding the inadequacy of paediatric data from clinical trials, which results in limited prescribing information available to support optimal pharmacotherapy for children. One explanation for the limited prescribing information in paediatrics is the perception of relatively infrequent use of drugs in the treatment of childhood conditions. To date there have been few population studies quantifying the extent of prescription drug use among Canadian children. Prescription information from administrative databases were available for over one million paediatric claimants likely representing approximately two million eligible children. Nearly 15% of Canadian children actually had a claim for a prescription drug recorded in this database. Descriptive statistics were employed to describe drug utilization for those who had a drug claim. The two largest therapeutic areas were antibiotics (76% of claimants) and respiratory drugs (18% of claimants). Among the top 20 drugs dispensed to paediatric claimants, 14 were antibiotics and the top drug overall was amoxicillin. Psychotropic drugs defined as stimulants, antidepressants, antipsychotics and anticonvulsants were dispensed to 6% of all paediatric claimants. In virtually every therapeutic area, the usage of drugs differed markedly across age groups. A wide range of drugs are prescribed to children, many of which are being used outside of the age ranges approved by Health Canada. However, most new drugs are used relatively infrequently in children. The large numbers of Canadian children documented in this study using prescription medicines reinforces the value of vigilance in ensuring that physicians are equipped with adequate information to ensure safe and effective prescribing. The use of therapeutic drugs is widespread, and for a large number of these drugs, safety and efficacy among children has not been established, although no conclusions can be drawn from this study about appropriateness of drug therapy in children. Consequently, ongoing vigilance in accurate paediatric labelling as well as future pharmacoepidemiologic studies addressing safety and efficacy are required. Efforts must be concentrated on those specific age ranges in which products or drug classes are used, evidence of which can be determined from actual utilization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".