Extended-release medications for children and adolescents with attention-deficit hyperactivity disorder
Bibliographic record
Abstract
Attention-deficit hyperactivity disorder (ADHD) affects one in 20 Canadian children, and is associated with unfavourable academic and employment records, high rates of injury and substance abuse, poor interpersonal relationships, poor mental health outcomes and poor quality of life. Medications have been shown to be efficacious in treating ADHD symptoms in controlled trials, and are associated with better social and health outcomes in observational studies. Extended-release (XR) medications for ADHD are preferred over short-acting immediate-release medications by many families and their treating physicians. The XR preparations are often unaffordable for affected families who are disproportionally among the lower socioeconomic strata.The objective of the present statement was to critically appraise the evidence for the relative effectiveness of XR versus immediate-release medications, and to make recommendations for their appropriate use in the treatment of ADHD.When medication is indicated, XR preparations should be considered as first-line therapy for ADHD because they are more effective and less likely to be diverted. Future research and cost-benefit analyses should consider both efficacy and effectiveness, and the diversion and misuse potentials of these medications. Industry, insurance companies and government must work together to make these medications accessible to all children and youth with ADHD.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 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".