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Record W3016645588 · doi:10.5498/wjp.v10.i4.34

Moderators and mediators of antipsychotic response in delusional disorder: Further steps are needed

2020· review· en· W3016645588 on OpenAlexaff
Alexandre González-Rodríguez, Armand Guàrdia, Diego Palao, Javier Labad, Mary V. Seeman

Bibliographic record

VenueWorld Journal of Psychiatry · 2020
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
FundersGeneralitat de Catalunya
KeywordsAntipsychoticComorbidityPsychiatryMedicineDelusional disorderSchizophrenia (object-oriented programming)Bipolar disorderClinical psychologyPsychologyPsychosisCognition

Abstract

fetched live from OpenAlex

Delusional disorder (DD) has been traditionally considered a relatively rare and treatment-resistant psychotic disorder. In the last decade, increasing attention has focused on therapeutic outcomes of individuals affected by this disorder. The aim of this paper is to provide a synthesis of the literature addressing two very important questions arising from DD research: (1) For which patients with DD do antipsychotic medications work best (the moderators of response); and (2) What variables best explain the relationship between such treatments and their effectiveness (the mediators of response). We searched PubMed and Google Scholar databases for English, German, French and Spanish language papers published since 2000. We also included a few classic earlier papers addressing this topic. Variables potentially moderating antipsychotic response in DD are gender, reproductive status, age, duration of illness, the presence of comorbidity (especially psychiatric comorbidity) and its treatment, brain structure, and genetics of neurochemical receptors and drug metabolizing enzymes. Antipsychotic and hormonal blood levels during treatment, as well as functional brain changes, are potential mediating variables. Some, but not all, patients with DD benefit from antipsychotic treatment. Understanding the circumstances under which treatment works best can serve to guide optimal management.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.330
Teacher spread0.309 · 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.

Study designOther design
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

Citations10
Published2020
Admission routes1
Has abstractyes

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