Vision screening of older drivers for preventing road traffic injuries and fatalities
Bibliographic record
Abstract
BACKGROUND: Demographic data in North America, Europe, Asia, Australia and New Zealand suggest a rapid growth in the number of persons over the age of 65 years as the baby boomer generation passes retirement age. As older adults make up an increasing proportion of the population, they are an important consideration when designing future evidence-based traffic safety policies, particularly those that lead to restrictions or cessation of driving. Research has shown that cessation of driving among older drivers can lead to negative emotional consequences such as loss of independence and depression. Those older adults who continue to drive tend to do so less frequently than other demographic groups and are more likely to be involved in a road traffic crash, probably due to what is termed the 'low mileage bias'. There is universal agreement among researchers that vision plays a significant role in driving performance, and that there are age-related visual changes. Vision testing of all drivers, and in particular of older drivers, is therefore an important road safety issue. The components of visual function essential for driving are acuity, field, depth perception and contrast sensitivity, which are currently not fully measured by licensing agencies. Furthermore, it is not known how effective vision screening tools are, and current vision screening regulations and cut-off values required to pass a licensing test vary from country to country. There is, therefore, a need to develop evidence-based tools for vision screening for driving, thereby increasing road safety. OBJECTIVES: To assess the effects of vision screening interventions for older drivers to prevent road traffic injuries and fatalities. SEARCH STRATEGY: We searched the Cochrane Injuries Group Specialized Register, the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2006, issue 3), MEDLINE, EMBASE, TRANSPORT, AgeInfo, AgeLine, the National Research Register, the Science (and Social Science) Citation Index, IBSS (International Bibliography of Social Sciences), PsycINFO, and Zetoc. We also searched the Internet and checked the reference lists of relevant papers to identify any further studies. The searches were conducted up to September 2006. SELECTION CRITERIA: Randomized controlled trials (RCTs) and controlled before and after studies comparing vision screening to non-screening of drivers aged 55 years and older, and which assessed the effect on road traffic crashes, injuries, fatalities and any involvement in traffic law violations, were included. DATA COLLECTION AND ANALYSIS: Two authors independently screened the reference lists for eligible articles and independently assessed the articles for inclusion against the criteria. Two authors independently extracted data using a standardized extraction form. MAIN RESULTS: No studies were found which met the inclusion criteria for this review. AUTHORS' CONCLUSIONS: Most countries require a vision screening test for the renewal of an individual's driver's license. There is, however, insufficient evidence to assess the effect of vision screening tests on subsequent motor vehicle crash reduction. There is a need to develop valid and reliable tools of vision screening that can predict driving performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".