Bibliographic record
Abstract
OBJECTIVE: In Quebec a driver whose acquired visual field defect renders them ineligible for a driver's permit renewal may request an exemption from the visual field standard by demonstrating safe driving despite the defect. For safety reasons it was decided to attempt to identify predictors of failure on the road test in order to avoid placing driving evaluators in potentially dangerous situations when evaluating drivers with visual field defects. METHODS: During a 4-month period in 2009 all requests for exemptions from the visual field standard were collected and analyzed. All available medical and visual field data were collated for 103 individuals, of whom 91 successfully completed the evaluation process and obtained a waiver. RESULTS: The collated data included age, sex, type of visual field defect, visual field characteristics, and concomitant medical problems. No single factor, or combination of factors, could predict failure of the road test. All 5 failures of the road test had cognitive problems but 6 of the successful drivers also had known cognitive problems. Thus, cognitive problems influence the risk of failure but do not predict certain failure. CONCLUSION: Most of the applicants for an exemption were able to complete the evaluation process successfully, thereby demonstrating safe driving despite their handicap. Consequently, jurisdictions that have visual field standards for their driving permit should implement procedures to evaluate drivers with visual field defects that render them unable to meet the standard but who wish to continue driving.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".