Self-perceived sleep during the Maintenance of Wakefulness Test: how does it predict accidental risk in patients with sleep disorders?
Bibliographic record
Abstract
STUDY OBJECTIVES: To determine whether the feeling of having slept or not during the Maintenance of Wakefulness Test (MWT) is associated with the occurrence of self-reported sleep-related traffic near misses and accidents in patients with sleep disorders. METHODS: This study was conducted in patients hospitalized in a French sleep center to perform a 4 × 40 min MWT. Relationship between mean sleep latency on the MWT, feeling of having slept or not during MWT trials and sleep-related near misses and accidents reported during the past year was analyzed. RESULTS: One hundred and ninety-two patients suffering from OSAS, idiopathic hypersomnia, narcolepsy, restless leg syndrome or insufficient sleep syndrome were included. One hundred and sixty-five patients presented no or one misjudgment of feeling of having slept during MWT trials while 27 presented more than two misjudgments. Almost half of the latter (48.1%) reported a sleepiness-related traffic near miss or accident in the past year versus only one third (27.9%) for the former (p < 0.05). Multivariate logistic regression showed that patients with more than two misjudgments had a 2.52-fold (95% CI, 1.07-5.95, p < 0.05) increase in the risk of reporting a sleepiness-related near miss/accident. CONCLUSIONS: Misjudgment in self-perceived sleep during the MWT is associated with the occurrence of self-reported sleepiness-related traffic near misses and accidents in the past year in patients suffering from sleep disorders. Asking about the perception of the occurrence of sleep during the MWT could be used to improve driving risk assessment in addition to sleep latencies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".