Machine Learning–Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial
Bibliographic record
Abstract
Background: The rising prevalence of dementia necessitates a scalable solution to cognitive assessments. The Autonomous Cognitive Examination (ACoE) is a foundational cognitive test for the phenotyping of cognitive symptoms across the primary cognitive domains. However, while the ACoE has been internally validated, it has not been externally validated in a clinical population, and its ability to render accurate appraisals of cognition is unknown. Further, it is unclear if these phenotypic assessments are useful in clinical tasks such as screening patients with and those without impairments. Objective: The objective of this study is to validate the ability of the ACoE to reliably phenotype cognition and to act as a screening examination relative to standard paper-based tests. Methods: To compare the evaluations of the ACoE to established paper-based tests, 46 patients with neurological disorders were enrolled in a randomized crossover study and received either the ACoE or a standard paper-based cognitive test. Patients received either the Addenbrooke Cognitive Examination-3 (ACE-3; n=35) or the Montreal Cognitive Examination (MoCA; n=11). We evaluated 3 primary metrics of the ACoE's performance relative to paper-based tests: (1) interrater reliability of overall cognitive scores, (2) interrater reliability of cognitive domain scores, and (3) ability to classify patients similarly to paper-based tests. Results: The ACoE's overall cognitive assessments were significantly reliable (ICC [intraclass correlation coefficient]=0.89; P<.001). Each cognitive domain's assessments were also significantly reliable, including attention (ICC=0.74; PFWE<.001), language (ICC=0.89; PFWE<.001), memory (ICC=0.91; PFWE<.001), fluency (ICC=0.74; PFWE<.001), and visuospatial function (ICC=0.78; PFWE<.001). The ACoE was also able to successfully diagnose patients similarly to both paper-based tests (area under the receiver operating characteristic curve=0.96; PFWE<.001). Conclusions: In this study, we evaluated if the ACoE could reliably phenotype cognitive symptoms relative to the assessments of established standard paper-based cognitive assessments. We found that the ACoE reliably phenotypes patient cognition, which can be used to screen patients. In the future, these cognitive phenotypes may be used to diagnose specific etiologies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".