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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".