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Record W2107612326 · doi:10.1007/s00787-010-0140-6

European guidelines on managing adverse effects of medication for ADHD

2010· article· en· W2107612326 on OpenAlexaff
Jaclyn Michelle Graham, Tobias Banaschewski, Jan K. Buitelaar, David Coghill, Marina Danckaerts, Regina Dittmann, Manfred Döpfner, Ruth Hamilton, Chris Hollis, Martin Holtmann, Martin Hulpke‐Wette, Michel Lecendreux, Éric Rosenthal, Aribert Rothenberger, Paramala Santosh, Joseph A. Sergeant, Emily Simonoff, Edmund Sonuga‐Barke, Ian Chi Kei Wong, Alessandro Zuddas, Hans‐Christoph Steinhausen, Eric Taylor

Bibliographic record

VenueEuropean Child & Adolescent Psychiatry · 2010
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids Foundation
FundersSanofiEli Lilly and Company
KeywordsHarmPsychiatryAdverse effectMedicinePsychologyIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

The safety of ADHD medications is not fully known. Concerns have arisen about both a lack of contemporary-standard information about medications first licensed several decades ago, and signals of possible harm arising from more recently developed medications. These relate to both relatively minor adverse effects and extremely serious issues such as sudden cardiac death and suicidality. A guidelines group of the European Network for Hyperkinetic Disorders (EUNETHYDIS) has therefore reviewed the literature, recruited renowned clinical subspecialists and consulted as a group to examine these concerns. Some of the effects examined appeared to be minimal in impact or difficult to distinguish from risk to untreated populations. However, several areas require further study to allow a more precise understanding of these risks.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0040.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0070.004

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 head0.026
GPT teacher head0.315
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations378
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Child & Adolescent PsychiatrySame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207