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Record W2097648013 · doi:10.1503/cmaj.1041064

Modern antipsychotic drugs: a critical overview

2005· review· en· W2097648013 on OpenAlexafffundvenue
David M. Gardner

Bibliographic record

VenueCanadian Medical Association Journal · 2005
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsDalhousie University
FundersDalhousie UniversityCollege of Pharmacy, Dalhousie UniversityBruce J. Anderson FoundationDepartment of Psychiatry, Faculty of Medicine, University of British ColumbiaMassachusetts General Hospital
KeywordsZiprasidoneTolerabilityOlanzapineRisperidoneQuetiapineAripiprazoleMedicineAntipsychoticClozapineAdverse effectPsychiatryAntipsychotic AgentAtypical antipsychoticIntensive care medicineSchizophrenia (object-oriented programming)Pharmacology

Abstract

fetched live from OpenAlex

Conventional antipsychotic drugs, used for a half century to treat a range of major psychiatric disorders, are being replaced in clinical practice by modern "atypical" antipsychotics, including aripiprazole, clozapine, olanzapine, quetiapine, risperidone and ziprasidone among others. As a class, the newer drugs have been promoted as being broadly clinically superior, but the evidence for this is problematic. In this brief critical overview, we consider the pharmacology, therapeutic effectiveness, tolerability, adverse effects and costs of individual modern agents versus older antipsychotic drugs. Because of typically minor differences between agents in clinical effectiveness and tolerability, and because of growing concerns about potential adverse long-term health consequences of some modern agents, it is reasonable to consider both older and newer drugs for clinical use, and it is important to inform patients of relative benefits, risks and costs of specific choices.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.050
GPT teacher head0.393
Teacher spread0.343 · 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

Citations418
Published2005
Admission routes3
Has abstractyes

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