Steering simulation performance in patients with obstructive sleep apnoea and matched control subjects
Bibliographic record
Abstract
Patients with obstructive pulmonary disease (OSA) have an increased rate of driving accidents, perhaps due to poor vigilance or impaired cognitive skills that influence their driving ability. The authors have assessed whether patients with OSA perform differently to control subjects on a steering simulator which allows the separate assessment of the two visual tasks required for steering a car, immediate positioning on road with reference to the road edges, and assessment of the curve of the oncoming road which allows faster driving. Twelve patients with OSA and 12 control subjects, matched for age, sex and driving experience, performed three 30-min drives with either all the oncoming road visible, only the near part of the road visible, or only the distant part of the road visible. Steering was assessed by measuring the SD around the theoretical perfect path (steering error) and the number of times the driver went "off road". Subjects identified the appearance of target numbers at the four corners of the screen as quickly as possible, thus making the test a divided attention task. Patients with OSA performed significantly less well on the three different road fields as measured by steering error (p<0.001), time to detect the target number (p<0.03), and off road events (p<0.03). The patients appeared to be particularly impaired on the two drives when only part of the road ahead was available to guide steering. This steering simulator, with its more realistic view of the road ahead, identifies impaired performance in patients with obstructive sleep apnoea. In addition it suggests that patients with obstructive sleep apnoea may be more disadvantaged compared to normal subjects when the view of the road ahead is limited (such as in fog).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".