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Record W3195057153 · doi:10.1186/s12888-021-03409-6

Secular trends and regional variations in pharmacotherapy of attention-deficit/hyperactivity disorder (ADHD) among children and adolescents in Germany

2021· article· en· W3195057153 on OpenAlexaff
Manas K. Akmatov, Jakob Holstiege, Jörg Bätzing

Bibliographic record

VenueBMC Psychiatry · 2021
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMethylphenidateAtomoxetineMedical prescriptionPharmacotherapyAttention deficit hyperactivity disorderMedicinePsychiatryPediatricsPopulationPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: The study aim was to examine the secular trends and regional variations in pharmacotherapy of children and adolescents with attention-deficit/hyperactivity disorder (ADHD) in Germany. METHODS: We used nationwide drug prescription data of outpatient care (2009 to 2016). The study population comprised patients aged between 5 and 14 years with the diagnoses "hyperkinetic disorders" (ICD-10 code F90) (e.g. n = 262,766 in 2016). The examined drugs were methylphenidate, amphetamines, atomoxetine and guanfacine. RESULTS: Overall, the proportion of patients received any prescription showed a decreasing trend over years (2010, 51%; 2016, 44%). The proportion of methylphenidate prescription was higher in Western than Eastern federal states. However, atomoxetine was more often prescribed in Eastern than Western federal states. The proportion of methylphenidate prescriptions issued by pediatric psychiatrists increased from 28% (2009) to 41% (2016). CONCLUSION: A decreasing trend in use of pharmacotherapy may be explained by prescription restrictions issued by the Federal Joint Committee in recent years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.308
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes1
Has abstractyes

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