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Record W4412672330 · doi:10.1017/9781009450966

Catatonia

2025· book· en· W4412672330 on OpenAlexaff
Dušan Hirjak, Georg Northoff

Bibliographic record

VenueCambridge University Press eBooks · 2025
Typebook
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCatatoniaMedicinePsychologyPsychiatrySchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

Catatonia is a complex neuropsychiatric disorder characterized by motor, affective and cognitive-behavioral symptoms, presenting significant challenges for both psychiatrists and neurologists. It occurs in 5–18% of patients in inpatient psychiatric units and in 3.3% of those in neurology or neuropsychiatric tertiary care inpatient settings. Despite its relatively high prevalence, catatonia is often underdiagnosed and inadequately treated, which can lead to substantial disadvantages for patients and may be associated with potentially life-threatening conditions. This comprehensive guide is designed to assist clinicians, researchers, and students in understanding and treating catatonia. It navigates through the history of the condition, exploring its phenomenology, clinical manifestations and pathophysiology, before delving into effective treatment strategies. By providing a clear and thorough overview, this guide simplifies the recognition of catatonia and promotes prompt and accurate treatment, encouraging future research endeavours in catatonia.

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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.177
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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