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Record W4415649732 · doi:10.1002/gps.70165

Development of the Clinical Insight Questionnaire: A Novel Clinical Tool for the Assessment of Insight Into Cognitive Symptoms and Everyday Functioning in People With Neurodegeneration

2025· article· en· W4415649732 on OpenAlexaboutno aff
Catherine Pennington, Harriet A. Ball, Peter Connelly, Elizabeth Coulthard, Gordon W. Duncan, Chineze Ivenso, Tobias Langheinrich, Vivek Pattan, Terence J. Quinn, Karen Ritchie, Tom C. Russ

Bibliographic record

VenueInternational Journal of Geriatric Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersParkinson's UKScottish Government Health and Social Care Directorate
KeywordsCognitionUsabilityPsychometricsCognitive impairmentCognitive Assessment SystemCognitive disorderCognitive testCognitive skill

Abstract

fetched live from OpenAlex

OBJECTIVES: Altered insight into cognitive symptoms and diagnosis is a common feature of neurodegeneration, and can adversely impact on quality of life and ability to access medical care. Affected individuals can lose awareness of their symptoms and therefore decline to engage with medical assessment and treatment. Assessing insight is difficult, and there is a lack of short, easily administered clinical assessment tools. The aim of this study was to develop and evaluate the feasibility of a novel insight assessment questionnaire (The Clinical Insight Questionnaire, CLIQ) which can be independently completed by adults with a range of cognitive abilities. METHODS: A discrepancy score approach was used to evaluate insight. A novel questionnaire targeting the domains of memory, personality and social behaviour, executive function, language, and activities of daily living was devised using the Delphi approach and public feedback. Participant and informant mirror versions of each item were written. The discrepancy between participant and informant scores provides an overall insight score. 12 UK based experts in cognitive disorder diagnosis and assessment were invited to review potential questionnaire items, as was a PPI group. A feasibility study was conducted where people with mild memory or thinking symptoms and an informant completed the questionnaire and the Montreal Cognitive Assessment. RESULTS: Following an iterative process using the expert Delphi group and public feedback, 20 final questionnaire items were selected from an initial pool of 30 items. 21 people with mild cognitive symptoms but no formally diagnosed cognitive disorder (median MoCA score 24.5) participated in feasibility testing. The mean discrepancy score was 1.14, close to the ideal score of zero. No participants found the assessment upsetting or too long, and 81% rated the questions as easy to understand. CONCLUSIONS: The Clinical Insight Questionnaire (CLIQ) is a novel clinical tool for the assessment of insight in people with mild to moderate neurodegeneration. In feasibility testing it was quick and easy for people with mild cognitive symptoms and informants to self-complete. Initial feasibility testing showed very promising findings for usability and acceptability, and a full validation study is now in progress.

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)0.005
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.407
Teacher spread0.367 · 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
GenreMethods

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

Citations1
Published2025
Admission routes1
Has abstractyes

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