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Record W2624220946 · doi:10.1155/2017/1364894

Adult ADHD: Questioning Diagnosis and Treatment in a Patient with Multiple Psychiatric Comorbidities

2017· article· en· W2624220946 on OpenAlexaff
Robert Karoly Chu, Tea Rosic, Zainab Samaan

Bibliographic record

VenueCase Reports in Psychiatry · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePsychiatryAttention deficit hyperactivity disorderDepression (economics)Mood disordersMoodMajor depressive disorderAdverse effectAttention deficitComorbidityPediatricsAnxietyInternal medicine

Abstract

fetched live from OpenAlex

Adult Attention-Deficit/Hyperactivity Disorder (ADHD) is a contentious diagnostic issue, which has been increasing in prevalence in recent years, and is often comorbid with other psychiatric disorders. This report presents a detailed account of a clinical case involving a middle-aged man with a history of recurrent depressive episodes and an unsubstantiated diagnosis of ADHD, treated with stimulants. There is persistent debate around the use of psychostimulants both in adult ADHD and in the treatment of depression. Despite promising activating properties, psychostimulants carry significant risks of misuse and substance use disorder. In this report, we consider the potential benefits and adverse effects of stimulants in the treatment of adult ADHD and mood disorders and review the learning points of this complicated, but not uncommon, clinical case.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.321
Teacher spread0.292 · 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 designCase report
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

Citations3
Published2017
Admission routes1
Has abstractyes

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