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Record W2905287543 · doi:10.1097/yco.0000000000000476

Psychosis in neurodegenerative disorders

2018· review· en· W2905287543 on OpenAlexaff
Wael K. Karameh, Geetanjali Murari, Tom A. Schweizer, David G. Muñoz, Corinne E. Fischer

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2018
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre for Addiction and Mental HealthSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsPsychosisNeurodegenerationDementiaDiseaseSchizophrenia (object-oriented programming)PsychiatryMedicineMovement disordersPsychologyNeurosciencePathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review presents the latest developments covered in the literature regarding psychosis in neurodegenerative disorders and discusses possible future research directions. RECENT FINDINGS: Recent findings in the field of psychosis and neurodegenerative disorders revolve around four main themes. The first theme is the impact of sex on the expression of psychosis in neurodegenerative disorders. The second theme focuses on the relationship between psychosis and neurodegenerative disease biomarkers. The third concerns how psychotic symptoms in neurodegenerative disorders may share common mechanisms with other primary psychotic disorders such as schizophrenia. Finally, there have been some promising developments in the area of therapeutics to treat dementia-related psychosis involving both established and novel treatments. SUMMARY: New findings in the field of neurodegeneration and psychosis parallel new directions in the field of neurodegeneration in general. More specifically, we have seen a shift in focus to issues highlighting the role of sex, biomarkers, translation to other disorders, and therapeutics.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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 head0.104
GPT teacher head0.451
Teacher spread0.347 · 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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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