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Record W2767533397 · doi:10.1002/mpr.1598

The Screen for Cognitive Impairment in Psychiatry: Proposal for a polytomous scoring system

2017· article· en· W2767533397 on OpenAlexaff
Juana Gómez‐Benito, Ángela I. Berrío, Georgina Guilera, Emilio Rojo, Scot E. Purdon, Óscar Pino

Bibliographic record

VenueInternational Journal of Methods in Psychiatric Research · 2017
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPolytomous Rasch modelCognitionSchizophrenia (object-oriented programming)PsychologyPsychiatryCognitive skillCognitive impairmentClinical psychologyItem response theoryPsychometrics

Abstract

fetched live from OpenAlex

The Screen for Cognitive Impairment in Psychiatry is a simple, fast, and easy to administer scale that has been validated in clinical and community samples. The aim of this study was to propose a polytomous scoring system for the Screen for Cognitive Impairment in Psychiatry and to demonstrate its functioning, thus providing new and complementary information regarding the utility and precision of this screening tool. Three hundred seventy-six Spanish patients diagnosed with schizophrenia spectrum disorder were evaluated. A polytomous scoring system was generated and analyzed by means of the partial credit model. Category assessment revealed optimal functioning after collapsing the 7-category system to 1 with either 5 or 4 categories, depending on the item. The proposed polytomous scoring system shows good psychometric properties and an adequate fit to the partial credit model. These results provide further confirmation of the test's utility in clinical settings and of its suitability for detecting cognitive impairment.

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)0.015
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.127
GPT teacher head0.562
Teacher spread0.435 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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