An International Review of Motor Vehicle Collision Risk with Medical Conditions in Older Adults
Bibliographic record
Abstract
Abstract The objective was to examine the impact of seven categories of medical illness on risk of Motor Vehicle Collisions (MVC) in older adults. In late 2019, a systematic review of the MVC risk associated with alcohol use disorders, psychiatric disorders, epilepsy, diabetes, hearing loss, vision disorders and sleep disorders was conducted. A total of 64,720 titles were screened, and 138 articles were included. Of these, only thirteen pertained to older adults, only six showed increased MVC risk in at least one condition, and only seven were rated of “Good” quality. Hearing impairment was associated with MVC only if associated with visual acuity or contrast sensitivity impairments (RR 1.52, 95% CI 1.01-2.3 and RR 2.41, 95% CI 1.62-3.57, respectively). A high depression score was associated with increased MVC (RR 1.5, 95% CI 1.1-2.1) in one study, but a similar relationship was not found in two other studies. Glaucoma increased at-fault MVC risk (RR 1.65, 95% CI 1.20-2.28) in one study, but no relationship was found in another. Visual field loss increased MVC risk in three of four studies (RR or HR ranging from 1.31 to 2.32). One negative study each were identified for alcohol use disorders, age-related macular degeneration, any eye disease, or any psychiatric disorder, and four negative studies were identified for reduced visual acuity. No studies of older adults were found for epilepsy or sleep disorders. Interpretation of MVC risk in older drivers with medical illness is rendered challenging by the paucity and quality of studies.
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 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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".