The Autonomous Cognitive Examination: Machine-Learning Based Cognitive Examination
Bibliographic record
Abstract
Structured Abstract INTRODUCTION The rising prevalence of dementia necessitates a scalable solution to cognitive screening and diagnosis. Digital cognitive assessments offer a solution but lack the extensive validation of older paper-based tests. Creating a digital cognitive assessment which recreates a paper-based assessment could have the strengths of both tests. METHODS We developed the Autonomous Cognitive Examination (ACoE), a fully remote and automated digital cognitive assessment which recreates the assessments of paper-based tests. We assessed its ability to reproduce entire cognitive screens in a comparison cohort (n = 35), and the ability to reproduce overall diagnoses with an additional validation cohort (n = 11). RESULTS The ACoE reproduced overall cognitive assessments with excellent agreement (intraclass correlation coefficient = 0.89) and reproduced overall diagnoses with excellent fidelity (area under the curve = 0.96). DISCUSSION The ACoE may reliably reproduce the evaluations of the ACE-3, which may help in accessible evaluation of patient cognition. Assessment in larger population of patients with specific diseases will be necessary to determine usefulness.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".