Medical Treatment of Children and Youths with Attention-Deficit/Hyperactivity Disorder (ADHD): A Norwegian Prescription Registry Based Study
Bibliographic record
Abstract
OBJECTIVES: Attention-deficit/hyperactivity disorder (ADHD) is a lifelong neurological condition with a profound effect on quality of life. Prescription databases may document pattern of use. In this study we aimed to explore the use in Norway employing such a database. METHODS: All prescriptions on drugs for the treatment of ADHD between 2004 and 2011, as registered in the Norwegian Prescription Database (NPD) were analyzed. The following drugs were included: Amphetamine, dexamphetamine, methylphenidate and atomoxetine. In-hospital drug administration was excluded. Numbers of users per 1,000 inhabitants were calculated according to gender, age and residence. A sub-analysis compared users born in January-June with those born in July-December. Drug costs were calculated and converted into Euros (€ 1 = N.kr 7.4540). RESULTS: Drugs for the treatment of ADHD was significantly more often prescribed in northern Norway than in any other Norwegian health region (P < 0.001). Within the northern region, Nordland County was the "culprit" (P < 0.02). Compared to Norwegian figures, significantly more females (aged 10-19 years) were treated in northern Norway [male/female ratios 3:1 and 2.2:1 (P < 0.01)] and especially in Nordland County (ratio 2.1:1). The subanalysis did not indicate a northern overtreatment of those being a younger group in their grade. The annual drug cost per user in Norway was € 919. CONCLUSIONS: The prescription rate was significantly higher in northern Norway and Nordland County was the culprit. A prescription database may be a tool for monitoring the national use of these drugs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".