Simulator Performance vs. Neurophysiologic Monitoring: Which is More Relevant to Assess Driving Impairment?
Notice bibliographique
Résumé
Previously, we reported on circadian variation in driving simulator performance and neurophysiologic evidence of sleep intrusion into consciousness in a pilot study of healthy individuals. We have since expanded this “normative” sample and run a prospective comparison study with a sample of clinical patients reporting excessive daytime sleepiness (EDS) as a chief complaint. Thirty healthy adults (mean age of 31.3 ± 11.5) and 27 EDS patients (47.0 ± 13.7) with valid driver’s licenses were included. Subjects performed four intentionally soporific 30-minute driving simulations at two-hour intervals while undergoing continuous EEG monitoring for microsleep (MS) episodes. Measured variables included: subjective ratings of sleepiness and alertness prior to each drive, lane position accuracy, mean speed, speed deviation, mean reaction time (RT) to “virtual” wind gusts as well as off-road events, i.e., “crashes.” In comparing normative individuals and EDS patients, significant between-group differences were found between subjective ratings, RT, crashes and MS. Both groups showed a significant a tendency towards RT slowing during afternoon drives, with this circadian effect appearing most pronounced for EDS patients. Significant between-group differences were also found on subjective ratings of sleepiness and alertness, although diurnal fluctuation of subjective sleepiness ratings was significant only for the EDS group. Objective EEG MS monitoring demonstrated escalating sleep intrusion with repeated drives in both groups, but particularly for the EDS group. Total crash rates were three times higher in EDS patients, with an increasing trend towards crash-proneness in the late afternoon. In summary, we found significantly impaired performance on some, though not all, driving parameters for EDS patients. While increased crash rate may be the most dramatic of these, slowing of RT was the most statistically robust. EEG monitoring was able to document increased propensity towards MS episodes in patients with EDS, which we suggest is causative in creating this impairment. It remains unclear whether a neurophysiologic or simulator approach captures impairment due to sleepiness with greater sensitivity and specificity. A hybrid approach combining data from both sources may be optimal, and also could be integrated in commercial vehicle use. We suggest that the need for a more accurate hospitalbased screening tool for assessment of driving impairment due to sleep disorders remains an important issue for physicians and legislators dealing with driving competency.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».