Validation of a German version of the dementia screening questionnaire for individuals with intellectual disabilities (DSQIID‐G) in Down's syndrome
Bibliographic record
Abstract
BACKGROUND: People with Down's syndrome (DS) are at high risk of developing Alzheimer dementia (DS-AD) due to a triplication of the amyloid precursor protein gene. While several tools to diagnose and screen for DS-AD, such as the dementia screening questionnaire for individuals with intellectual disabilities (DSQIID), are available in English, validated German versions of such instruments are scarce. METHODS: A German version of the DSQIID questionnaire (DSQIID-G) was completed by caregivers before attending our specialist outpatient department for DS-AD. All participants were assessed blind to DSQIID-G scoring using clinical and neuropsychological examinations, including the Cambridge Examination for Mental Disorders of Older People with Down's Syndrome and Others with Intellectual Disabilities (CAMDEX-DS). ICD-10 and amyloid/tau/neurodegeneration (A/T/N) criteria were applied to detect and categorise cognitive decline. RESULTS: Of 86 participants, 43 (50%) showed evidence of cognitive decline. A definite diagnosis of DS-AD was reached in 17 (19.8%) and mild cognitive impairment in seven (8.3%) participants. Secondary causes of cognitive decline were determined among 13 (15.1%) participants, and in six (7%) cases, the diagnosis remained unclassifiable due to co-morbidities. Compared with cognitively stable individuals, participants with cognitive decline (n = 43) displayed higher DSQIID-G total scores [median (range): 3 (0-21) vs. 19 (0-48), P < 0.001]. A total score of >7 provided a sensitivity of 0.94 against a specificity of 0.76, to discriminate DS-AD and participants without cognitive decline according to ROC analysis. The convergent validity against the CAMDEX-DS interview score was good (r = 0.74), and split-half reliability (r = 0.96), internal consistency (Cronbach's α r = 0.96), test-retest reliability (r = 0.88) (n = 25) and interrater reliability (r = 0.81) (n = 31) were excellent. CONCLUSIONS: The DSQIID-G showed excellent psychometric properties, including concurrent and internal validity and reliability. The cut-off value for screening was lower than in the original English validation study. For a screening instrument like DSQIID-G, a lower cut-off is preferable to increase case detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".