Introducing practical tools for fit to drive assessment of the elderly: A step toward improving the health of the elderly
Bibliographic record
Abstract
Today, as age increases, the demand for independent living has increased. Since driving is one of the safest and preferred ways for the elderly to travel, paying close attention to the accurate assessment of the elderly's driving ability can prevent traffic accidents in this age group. The purpose of this study was to identify and introduce practical tools for drive assessment fitness of the elderly. This systematic review was conducted according to Cochrane methodology and reported findings according to PRISMA. The following databases were searched from PubMed, ISI web of knowledge, Scopus, ProQuest, Medlib, SID, Magiran, Iran doc, and Iran Medex based on the population intervention comparison outcome method. The total records involving 12 main tools were assessed from 26 selected records in the final evaluation. The research findings indicated the selection of seven tools in the psycho-cognitive function domain such as TMT-B, Clock Drawing Test, MAZE, Montreal Cognitive Assessment, GDS-15, MMSE, and ACE-R, three tools in the sensory function domain such as Snellen, Confrontation Visual field, and Whispered Voice Test, and also two tools in motor function domain such as Rapid pace walk, and Manual test of the range of motion. The findings led to selecting practical, accurate, and fast tools for widespread use for the assessment of driving competencies of the elderly. Therefore, it is recommended that the selected tools be used in practical batteries to assess the driving skills of the elderly.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".