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Record W3127437160 · doi:10.12788/acp.0015

Retrospective Review of Use of Adjunctive Psychostimulants in Patients with Schizophrenia

2021· article· en· W3127437160 on OpenAlexaff
Naista Zhand, Philip D. Harvey, Roisin Osborne, Anna Hatko, Marika Stuyt, Alain Labelle

Bibliographic record

VenueAnnals of Clinical Psychiatry · 2021
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCarleton UniversityUniversity of OttawaRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)PsychosisAdjunctive treatmentRetrospective cohort studyPsychiatryCognitionAdverse effectMedicineMethylphenidatePsychologyClinical psychologyAttention deficit hyperactivity disorderInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Adjunctive psychostimulants have been proposed as a potential treatment option for the management of cognitive and/or negative symptoms of schizophrenia. METHODS: The present study is a retrospective review of use of adjunctive psychostimulants among outpatients enrolled in our tertiary Schizophrenia Program between 2014 and 2019. We assessed response to treatment, adverse effects, and the impact of various clinical factors on treatment outcome. RESULTS: Of the 77 (out of 1,300) participants prescribed psychostimulants during the study period, 42.22% had chart-based evidence of significant improvement, 27.77% had minimal improvement, and 25.55% reported no change. The majority (61.9%) demonstrated improvement in attention, concentration, and/or other cognitive symptoms. Approximately one-third of cases had evidence of emergence of psychosis. Of the factors assessed, comorbid attention-deficit/hyperactivity disorder was associated with an increased likelihood of response, and higher doses of stimulants were associated with likelihood of emergence of psychosis. CONCLUSIONS: Adjunctive psychostimulants could be a potential treatment consideration to address cognitive deficits in selected patients with schizophrenia.

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)0.002
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 score0.021
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.115
GPT teacher head0.436
Teacher spread0.321 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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