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Record W71528383 · doi:10.1177/070674370200601s11

Guidelines and algorithms for the use of methylphenidate in children with Attention-Deficit/Hyperactivity Disorder

2002· review· en· W71528383 on OpenAlexaff
Laurence L. Greenhill, D. Beyer, J. Finkleson, D. Shaffer, Joseph Biederman, C. Keith Conners, Peter S. Jensen, James L. Kennedy, Rachel G. Klein, Jack Rapoport, Terje Sagvolden, Thomas Spencer, James M. Swanson, Nora D. Volkow

Bibliographic record

VenueJournal of Attention Disorders · 2002
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthNational Institutes of HealthNational Institute for Health and Care Research
KeywordsMethylphenidateAttention deficit hyperactivity disorderPsychiatryPsychologyClinical psychologyAttention deficitAlgorithmComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: To review published algorithms for guiding the use of methylphenidate (MPH) in the treatment of Attention-Deficit/Hyperactivity Disorder (ADHD) in children and adolescents. METHODS: A consensus roundtable of 12 experts was convened to review the evidence for the safety and efficacy of MPH in the treatment of ADHD, as well as the published algorithms and practice guidelines for using MPH. The experts reviewed the algorithms for practicality and acceptability by clinicians. RESULTS: Algorithms that included MPH commonly selected it as the initial medication to be employed in the treatment of children with ADHD. Factors involved included its high efficacy, good safety record, and the ubiquitous nature of its appearance in the ADHD treatment literature. CONCLUSIONS: MPH should be considered as the first medication to be used in a treatment algorithm for children and adolescents with ADHD.

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)0.016
metaresearch head score (Gemma)0.049
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: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.049
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0080.005
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0070.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.002

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 head0.141
GPT teacher head0.387
Teacher spread0.246 · 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
GenreReview

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

Citations80
Published2002
Admission routes1
Has abstractyes

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