Digital Screening for Cognitive Impairment — A Proof of Concept Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Due to an ageing demographic and rapid increase of cognitive impairment and dementia, combined with potential disease-modifying drugs and other interventions in the pipeline, there is a need for the development of accurate, accessible and efficient cognitive screening instruments, focused on early-stage detection of neurodegenerative disorders. OBJECTIVE: In this proof of concept report, we examine the validity of a newly developed digital cognitive test, the Geras Solutions Cognitive Test (GCST) and compare its accuracy against the Montreal Cognitive Assessment (MoCA). METHODS: 106 patients, referred to the memory clinic, Karolinska University Hospital, due to memory complaints were included. All patients were assessed for presence of neurodegenerative disorder in accordance with standard investigative procedures. 66% were diagnosed with subjective cognitive impairment (SCI), 25% with mild cognitive impairment (MCI) and 9% fulfilled criteria for dementia. All patients were administered both MoCA and GSCT. Descriptive statistics and specificity, sensitivity and ROC curves were established for both test. RESULTS: Mean score differed significantly between all diagnostic subgroups for both GSCT and MoCA (p<0.05). GSCT total test time differed significantly between all diagnostic subgroups (p<0.05). Overall, MoCA showed a sensitivity of 0.88 and specificity of 0.54 at a cut-off of <=26 while GSCT displayed 0.91 and 0.55 in sensitivity and specificity respectively at a cut-off of <=45. CONCLUSION: This report suggests that GSCT is a viable cognitive screening instrument for both MCI and dementia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it