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Record W4391650166 · doi:10.1177/10870547231224088

Investigating Variations in Medicine Approvals for Attention-Deficit/Hyperactivity Disorder: A Cross-Country Document Analysis Comparing Drug Labeling

2024· article· en· W4391650166 on OpenAlexaboutno aff
Laila Tanana, Asam Latif, Prasad S. Nishtala, Timothy F. Chen

Bibliographic record

VenueJournal of Attention Disorders · 2024
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsAttention deficit hyperactivity disorderAttention deficit disorderAttention deficitPsychologyPsychiatryDrugCross countryClinical psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to compare the approval of medicines for attention deficit/hyperactivity disorder (ADHD) for pediatric patients across five countries. METHOD: A document analysis was completed, using the drug labeling for ADHD medicines from five countries; United Kingdom, Australia, New Zealand, Canada and United States (US). Comparisons of available formulations and approval information for ADHD medicine use in pediatric patients were made. RESULTS: The US had the highest number of approved medicines and medicine forms across the studied countries (29 medicine forms for 10 approved medicines). Approved age and dosage variations across countries and missing dosage information were identified in several drug labeling. CONCLUSIONS: The discrepancies in approval information in ADHD medicine drug labeling and differing availability of medicine formulations across countries suggest variations in the management of ADHD across countries. The update of drug labeling and further research into reasons for variability and impact on practice are needed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.364
Teacher spread0.333 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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