The Role of Reduced Fitness to Drive Due to Medical Impairments in Explaining Crashes Involving Older Drivers
Bibliographic record
Abstract
Background. Medical conditions and associated impairments are known to be more prevalent with aging and can potentially impact the function and crash risk of older drivers. Objectives. To evaluate the impact of specific medical conditions and associated impairments on older driver crash risk. Methods. A search identified reports and peer-reviewed publications evaluating the risk for medical conditions and associated crash risk. Medical conditions associated with older persons were reviewed to determine the associated relative risk of crash. Results. The review identified three recent comprehensive reviews of medical conditions or chronic illnesses and crash risk: Dobbs (2005) Dobbs, B M. 2005. Medical Conditions and Driving: A Review of the Scientific Literature (1960–2000) Technical Report for the National Highway and Traffic Safety Administration and the Association for the Advancement of Automotive Medicine Project, Washington, DC [Google Scholar]; Vaa (2003) Vaa, T. 2003. Impairment, Diseases, Age and Their Relative Risks of Accident Involvement: Results from Meta-Analysis TØI Report 690 for the Institute of Transport Economics, Oslo, Norway [Google Scholar]; Charlton et al. (2004) Charlton, J, Koppel, S, O'Hare, M, Andrea, D, Smith, G, Khodr, B, Langford, J, Odell, M and Fildes, B. 2004. Influence of Chronic Illness on Crash Involvement of Motor Vehicle Drivers, Clayton, , Australia: Monash University Accident Research Centre. Report No. 213 [Google Scholar]. Comparison of the reviews reveals a relatively high agreement where medical conditions considered to be at slightly to moderately increased relative risk of crash include alcohol abuse and dependence, cardiovascular disease, cerebrovascular disease/TBI, depression, dementia, diabetes mellitus, epilepsy, use of certain medications, musculoskeletal disorders, schizophrenia, obstructive sleep apnea, and vision disorders. However, determining fitness to drive at the individual level based on diagnosis has significant limitations related to factors such as multiple medical conditions as well as varying severity of disease and associated functional impairments. Medical conditions that may affect driving can serve as “red flags” to assist health care professionals and driving administrators to identify drivers who may need further evaluation. Conclusions. Medical conditions overall, do impact the fitness to drive of older drivers; however, the crash risk tends to be only slightly to moderately increased. The conditions can serve as potential warnings for reduced fitness to drive, but many persons with these medical conditions would still be considered safe to continue driving.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".