Collisions Involving Senior Drivers: High-Risk Conditions and Locations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To better understand the characteristics of crashes involving senior drivers 65 and older, studies of these crashes were reviewed. METHODS: The review focused primarily on North American studies published since 1990. Studies point to important differences between the crashes of senior drivers and those of younger drivers. RESULTS: Numerous studies have found that senior drivers' crashes are much more likely than crashes of younger drivers to occur at intersections. Senior drivers have particularly high rates of involvement in intersection crashes when they are turning, and even more so when they are turning left. Senior drivers are more likely than younger drivers to have been at fault in these situations, typically because they failed to yield the right-of-way, disregarded the traffic signal, or committed some other traffic violation. Studies also suggest that the extent of overinvolvement of senior drivers in certain types of crashes generally increases with advancing age. CONCLUSIONS: The extent to which the distinctive characteristics of senior drivers' crashes may be due to changing travel patterns associated with aging, or physical or cognitive impairments related to the aging process, is unclear. Further research is needed to understand the pre-crash circumstances of older drivers' intersection crashes.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it