Utility of the MOCA as a cognitive predictor for fitness to drive
Bibliographic record
Abstract
Determining fitness to drive is a major concern affecting aging and disabled populations, particularly concerning reduced cognitive functioning, functional limitations and reduced vision [1, 2]. The Royal Society for Prevention of Accidents encourages aging drivers to maintain their licence (for independence, mobility and quality of life), emphasising that prematurely removing someone’s driving licence negatively affects their quality of life - the consequences of which outweigh the chance of being involved in a collision, for both the driver and the remainder of society [3].\nThe gold standard test in the United Kingdom (UK) to determine the ability to drive is an on-road driving assessment, and clinicians have the opportunity to refer patients to an independent Mobility Centre (accredited by Driving Mobility) where an assessment will be performed based upon on-road driving experience as judged by a professional driving instructor and occupational therapist[4]. The assessment is resource expensive and only a limited number of individuals are referred. To date no screening test is clinically implemented in the UK which accurately determines fitness to drive[4].\nThis study sets out to evaluate the potential of the Montreal Cognitive Assessment (MOCA) as a screening tool, for people with concerns regarding cognitive capacity; to determine pass/fail cuts offs for on-road driving assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.008 |
| 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; both teacher heads agree on what is shown here.
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".