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Record W2122790466 · doi:10.2217/14796708.1.3.249

Atomoxetine in clinical practice

2006· article· en· W2122790466 on OpenAlexaff
Margaret D. Weiss, Adil Virani, Michael Wasdell, Lorelei Faulkner, Kathleen Rea, Roger D. Fréeman, Gabrielle Weiss, Veena Jokhani

Bibliographic record

VenueFuture Neurology · 2006
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsChildren's & Women's Health Centre of British ColumbiaUniversity of British Columbia
FundersEli Lilly and Company
KeywordsAtomoxetineStimulantMedicineMethylphenidateGuanfacineContraindicationComorbidityAnxietyPsychiatryAnesthesiaAttention deficit hyperactivity disorderClonidine

Abstract

fetched live from OpenAlex

The authors provide clinical suggestions for optimizing the use of atomoxetine in practice. Atomoxetine is a highly specific norepinephrine inhibitor that was developed for the treatment of attention-deficit/hyperactivity disorder (ADHD) in all age groups. In clinical trials, it has a slightly lower effect size (0.2) than stimulants, but these studies exclude patients who are stimulant nonresponders as well as patients with comorbidities that constitute a relative contraindication to stimulants, such as tics, anxiety and substance use. If these common comorbid conditions were included, the outcome of the studies might have been different. Side effects, such as nausea, vomiting and stomach ache, may be mitigated by giving the medication on a full stomach, while sedation may be mitigated by giving the medication in the evening. Time course to response for atomoxetine is slower than that of stimulants, but a positive outcome provides for more consistent coverage over a 24-h period. Atomoxetine may be combined with stimulants to optimize full-day coverage with a booster effect on symptoms during the day. No studies to date examining real-life effectiveness outcomes of atomoxetine versus stimulants have examined quality of life, functioning, impact on comorbidity and long-term persistence with medication.

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)0.001
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 score0.424
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.366
Teacher spread0.344 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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