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Record W4414208021 · doi:10.1192/j.eurpsy.2025.211

Assessing CIAS in clinical routine

2025· article· en· W4414208021 on OpenAlexaboutno aff
Andreas Erfurth, Gabriele Sachs

Bibliographic record

VenueEuropean Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPsychopathologyCognitionCognitive impairmentSchizophrenia (object-oriented programming)RisperidoneBipolar disorderCognitive Assessment System

Abstract

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Abstract Cognitive impairment associated with schizophrenia (CIAS) is a relevant problem and can be well assessed in different ways, as described in an EPA guidance paper (1). In routine clinical practice, the clinician usually relies on targeted questions to elicit psychopathological findings, as described in the AMDP system (2), for example. If there is a need for a more precise assessment of cognitive dysfunction, also in a transdiagnostic way, the Screen for Cognitive Impairment in Psychiatry (SCIP) (3) has recently been used by several working groups. In a recent pilot study (4), we used the German version of the SCIP (SCIP-G) (5,6) to assess cognitive impairment in acute psychiatric inpatients diagnosed with psychotic disorders, bipolar disorder and depression. (1) Vita A, Gaebel W, Mucci A, Sachs G, Erfurth A, Barlati S, Zanca F, Giordano GM, Birkedal Glenthøj L, Nordentoft M, Galderisi S. European Psychiatric Association guidance on assessment of cognitive impairment in schizophrenia. Eur Psychiatry. 2022 Sep 5;65(1):e58. doi: 10.1192/j.eurpsy.2022.2316. (2) The AMDP System: Manual for Assessment and Documentation of Psychopathology in Psychiatry, 9th edition. 2017. Ed.: Broome MR, Bottlender R, Rösler M, Michael; Stieglitz RD. Hogrefe Publishing GmbH. ISBN: 978-0-88-937542-0 (3) Purdon SE. The Screen for Cognitive Impairment in Psychiatry (SCIP): Instructions and three alternate forms. 2005. PNL Inc, Edmonton, Alberta (4) Maihofer EIJ, Sachs G, Erfurth A. Cognitive Function in Patients with Psychotic and Affective Disorders: Effects of Combining Pharmacotherapy with Cognitive Remediation. J Clin Med. 2024 Aug 16;13(16):4843. doi: 10.3390/jcm13164843. (5) Sachs G, Lasser I, Purdon SE, Erfurth A. Screening for cognitive impairment in schizophrenia: Psychometric properties of the German version of the Screen for Cognitive Impairment in Psychiatry (SCIP-G). Schizophr Res Cogn. 2021 May 12;25:100197. doi: 10.1016/j.scog.2021.100197. (6) Sachs G, Bannick G, Maihofer EIJ, Voracek M, Purdon SE, Erfurth A. Dimensionality analysis of the German version of the Screen for Cognitive Impairment in Psychiatry (SCIP-G). Schizophr Res Cogn. 2022 Jun 6;29:100259. doi: 10.1016/j.scog.2022.100259. Disclosure of Interest None Declared

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)0.011
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.045
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.006

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 head0.029
GPT teacher head0.392
Teacher spread0.363 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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