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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".