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Record W2804577629 · doi:10.1055/a-0626-7135

Discrepancies Between Nomenclature and Indications of Psychotropics

2018· article· en· W2804577629 on OpenAlexaff
Fusaka Minami, Joseph Zohar, Takefumi Suzuki, Teruki Koizumi, Masaru Mimura, Gohei Yagi, Hiroyuki Uchida

Bibliographic record

VenuePharmacopsychiatry · 2018
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychiatryPanic disorderBipolar disorderAnxietyDepression (economics)MedicinePsychologyLithium (medication)

Abstract

fetched live from OpenAlex

INTRODUCTION: While the current nomenclature of psychotropic drugs is disease-based, their approved indications do not always match their classifications. METHODS: Information on approved indications of "second-generation antipsychotics" and "newer antidepressants" that are available in the United States (US), the United Kingdom (UK), France, Germany, and Japan were extracted from their packet inserts. RESULTS: A significant proportion of "atypical antipsychotics" were approved for psychiatric conditions other than psychotic disorders (i. e., bipolar disorder, major depressive disorder, and autistic disorder) as follows: 76.9% in the US, 66.7% in the UK, 66.7% in France, 60.0% in Germany, and 44.4% in Japan. Likewise, more than half of "newer antidepressants" had approved indications for psychiatric conditions other than depression (e. g., panic disorder, obsessive compulsive disorder, social anxiety disorder, general anxiety disorder, and post-traumatic stress disorder): 56.3% in the US, 69.2% in the UK, 69.2% in France, 50.0% in Germany, and 62.5% in Japan. CONCLUSIONS: Our results raise concerns regarding generic terminologies of "antipsychotics" and "antidepressants" since the conventional indication-based nomenclature does not fit well with the official indication.

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)0.000
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 score0.011
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.019
GPT teacher head0.353
Teacher spread0.333 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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