Minimal Impact of Inadvertent Sleep Between Naps on the MSLT and MWT
Bibliographic record
Abstract
Sleepiness is often neurophysiologically assessed using the multiple sleep latency test (MSLT) or the maintenance of wakefulness test (MWT). We examined the frequency of incidental intersession napping during MSLT and MWT testing to see if there was a relationship between intersession napping, mean sleep latency and subjective sleepiness on the Epworth Sleepiness Scale (ESS). We conducted a retrospective analysis of 24 studies of subjects who underwent either a MSLT or a MWT as a component of their clinical assessment and had coincidental wireless telemetry recording of their sleep in between scheduled naps. We found that 17.6% of the MSLT patients and 28.6% of the MWT patients slept inadvertently between test sessions. The group of patients who napped between sessions had shorter sleep latencies on the MSLT. No statistically significant group-wise difference between the sleep latencies of those who napped between MWT sessions and those who did not was found. There was no significant difference between the ESS of those who did and those who did not sleep between sessions. We found that brief inadvertent intersession napping was common during the MSLT and MWT, but there was no evidence to suggest that this significantly alters clinical test results.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".