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Record W4210685971 · doi:10.15273/dmj.vol48no1.11259

Amphetamines in the treatment of adult attention-deficit/ hyperactivity disorder (ADHD) and cocaine use disorder (CUD): The role of pharmacists

2022· article· en· W4210685971 on OpenAlexvenueno aff
Athena Milios

Bibliographic record

VenueDalhousie Medical Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsImpulsivityAttention deficit hyperactivity disorderPsychiatryAmphetamineMethylphenidatePsychologySubstance abuseMedicineAttention deficitDrugClinical psychologyNeuroscience

Abstract

fetched live from OpenAlex

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by inattentiveness, hyperactivity, and impulsivity. This review paper outlines the role that pharmacists can play in monitoring amphetamine use, to reduce the possibility of medication abuse by those with ADHD and/or Cocaine Use Disorder (CUD). Because individuals with ADHD also struggle with impulsivity, they are more likely to abuse substances, particularly illegal stimulants (such as cocaine), in an effort to self-medicate. This article also reviews the pharmacokinetics of amphetamine derivatives as well as the evidence for their use to manage ADHD and CUD. Neuropharmacologically, the proposed mechanism of action of amphetamines in the treatment of CUD is also detailed. Finally, theimplications of these findings for pharmacy practice are discussed. The primary findings and principal conclusions are that amphetamines have been found to improve both ADHD and CUD symptomatology, primarily through increasing DA release from nerve terminals in the central nervous system, as well as increasing the release of NE and serotonin. Pharmacists can play an important role in monitoring use of these medications by working in collaboration with family physicians and psychiatrists to ensure that ADHD/CUD patients are taking their amphetamines as prescribed.

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)0.001
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 score0.073
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.024
GPT teacher head0.311
Teacher spread0.287 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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