Development and Validation of the IC3: An Online Remote Assessment Technology for Deep Phenotyping and Monitoring of Cognitive Impairment After Stroke
Bibliographic record
Abstract
Automated cognitive assessments tailored to specific clinical scenarios have the potential to revolutionize health care and clinical research. Stroke survivors experience significant burden from underdiagnosed cognitive deficits. To address this, we developed a digital cognitive battery (IC3 [the Imperial Comprehensive Cognitive Assessment in Cerebrovascular Disease]) highly optimized for stroke survivors, and specifically designed for unsupervised administration in patients with mild to moderate stroke, thus enabling detailed remote diagnosis and monitoring of a variety post-stroke cognitive impairments. In a study involving 90 stroke survivors and over 6,000 age-matched healthy adults, the battery demonstrated high concordance with the Montreal Cognitive Assessment (MoCA), a commonly used supervised clinical neuropsychological assessment ( r = .58, p < .001) and close correlation with patients’ quality of life ( r = .51, p < .001). In patients deemed to be cognitively unimpaired based on the standard MoCA cut-off (≥26/30, education-corrected), IC3 detected prevalence of impairment as high as 54% in a subset of tasks ( M = 30.2%, range = 4%–54%). Importantly, performance on the IC3 remained consistent in both supervised and unsupervised settings in the controls, with minimal learning effects over time. This work provides the first evidence of the robustness and clinical potential of this technology for remote application in stroke, and potentially other neurological settings.
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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 | 0.000 | 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 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".