Neuroimaging of Narcolepsy and Primary Hypersomnias
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advances in neuroimaging open up the possibility for new powerful tools to be developed that potentially can be applied to clinical populations to improve the diagnosis of neurological disorders, including sleep disorders. At present, the diagnosis of narcolepsy and primary hypersomnias is largely limited to subjective assessments and objective measurements of behavior and sleep physiology. In this review, we focus on recent neuroimaging findings that provide insight into the neural basis of narcolepsy and the primary hypersomnias Kleine-Levin syndrome and idiopathic hypersomnia. We describe the role of neuroimaging in confirming previous genetic, neurochemical, and neurophysiological findings and highlight studies that permit a greater understanding of the symptoms of these sleep disorders. We conclude by considering some of the remaining challenges to overcome, the existing knowledge gaps, and the potential role for neuroimaging in understanding the pathogenesis and clinical features of narcolepsy and primary hypersomnias.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it