Enhancements to the multiple sleep latency test
Bibliographic record
Abstract
INTRODUCTION: The utility of multiple sleep latency tests (MSLTs) is limited to determining sleep onset latency (SOL) and rapid eye movement sleep latency. The odds ratio product (ORP) is a continuous index of sleep depth with values of 0, 1.0, and 2.5 reflecting very deep sleep, light sleep, and full wakefulness, respectively. We determined the time course of sleep depth during MSLT naps expecting that this would enhance the test's clinical utility. METHODS: Thirty MSLTs (150 naps) were performed for excessive somnolence. Patients indicated whether they slept (yes/no) after each nap. SOL was scored by two experienced technologists. Time course of ORP was determined with a commercial system. We determined ORP at SOL (ORPSOL), times ORP decreased <2.0, <1.5, <1.0 and <0.5 during the entire nap duration, and the integral of decrease in ORP over nap duration (ΔORPINT). RESULTS: SOL occurred almost invariably when ORP was between 1.0 and 2.0. Of 47 naps (21 patients) with SOL <5 minutes, ORP decreased <1.0 (light sleep) in <5 minutes in only 13 naps (nine patients) and <0.5 (deep sleep) in only two naps in one patient. The relation between ORPINT and frequency of sleep perception was well defined, allowing determination of a threshold for sleep perception. This threshold ranged widely (5-50 ΔORP*epoch). CONCLUSION: As currently identified, SOL reflects transition into a highly unstable state between wakefulness and sleep. Reporting the times of attaining different sleep depths may help better identify patients at high risk of vigilance loss. Furthermore, an ORPSOL outside the range 1.0-2.0 can help identify scoring errors.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".