Bibliographic record
Abstract
Dementia and Mild Cognitive Impairment (MCI) are significant health issues that are a rising cost to society.They are monitored with cognitive tests during clinical appointments that are limited by the healthcare system capacity and patient's ability/willingness to attend.Current cognitive tests use behavioural measures and not direct measures of underlying cellular change.This causes delay in identification through a patient's ability to compensate (reminder notes) to mask symptoms.This work presents measurement methods for cognition between clinical appointments using an integrated approach for episodic cognition assessment.Methods assess the patient during Instrumental Activities of Daily Living (IADL) within an episodic measurement framework.Electroencephalogram (EEG) / Event Related Potential (ERP) methods are presented as an emerging alternative means to detect changes in the brain.Recent consumer EEG devices make at home use a future possibility.ERP features for healthy and MCI volunteers are defined, analyzed and machine learning identified two features to distinguish the two cases with 1 False Positive and 1 False Negative error in a group of 32 subjects.The measurement of two IADLs is presented: Computer game play and Driving.Two games were developed and piloted with MCI volunteers showing they could indicate cognitive change.The work presents game design needs including hint and measurement subsystems.Driving is a complex task that combines executive cognitive tasks (navigation) with over-learned cognitive tasks (turn signal use).The work presents measures of driving behavior creating a driver unique signature.Machine learning techniques show that the features will allow two drivers of a shared vehicle to be distinguished from each other with an error rate as low as 1.5%.Navigational performance measures are presented for driver trip planning to indicate executive function supervisors
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".