OVERVIEW OF MEDICAL FITNESS TO DRIVE IN STRATEGIC AGE-RELATED DISEASE
Bibliographic record
Abstract
The ability to drive a car has become a key element of retaining independence and supporting well-being and social connection at all ages, and particularly so in later life. Although older drivers have an enviable safety record, it is increasingly common for geriatricians to be faced with the issue of determining fitness to drive among patients presenting to their services. Developing the body of knowledge to inform such decisions is an emerging element of academic geriatric medicine. This symposium provides a synthesis from four academic geriatricians active in Europe and North America in developing research on driving with a particular focus on two key relevant age-related syndromes, stroke and dementia. Prof Des O’Neill will present an overview on a new European Commission report on older drivers, Dr Dorota Religa will present on data from the Swedish national registers of stroke and dementia on advice provided by physicians on driving, while Drs Carr and Marottoli will present on major international systematic reviews on the risks associated with stroke and dementia which have been prepared by the group. Following the symposium, participants will have gained insights into the latest research into driving with stroke and dementia, as well as knowledge of the structures and processes which need to be developed to support the provision of safe mobility to our patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".