Testing sleepiness and vigilance in the sleep laboratory
Bibliographic record
Abstract
PURPOSE OF REVIEW: Excessive daytime sleepiness (EDS) is common and a potentially devastating public health challenge. EDS has been implicated as a contributing factor to workplace injury, motor vehicle accidents, cardiovascular disease, and impaired quality of life. Subjective self-report measures have failed to sufficiently quantify EDS. The use of objective tools found in sleep laboratories is therefore fundamental in the management of patients with EDS. The purpose of this review is to provide an overview of the current methods used to quantify sleepiness, and to highlight recent advances. RECENT FINDINGS: The Multiple Sleep Latency Test (MSLT), normally used for the diagnosis of narcolepsy, can be a useful tool in recognizing other forms of sleepiness. The Maintenance of Wakefulness Test (MWT) has also been confirmed as an important test to identify EDS, as well as to provide an indicator of future risk of accidents. Modifications and newer tests have been discussed with potential applications for the future. SUMMARY: Objective tests such as the MSLT and MWT are useful in the diagnosis and management of patients with EDS. However, the relatively high cost can restrict their overall usefulness in clinical medicine. Newer simple tests are under development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".