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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".