Diagnosing and treating hypersomnolence in depression
Bibliographic record
Abstract
Hypersomnolence, a broad presentation encompassing excessive daytime sleepiness, hypersomnia and sleep inertia, affects around 25 % of patients with a major depressive disorder. Yet, hypersomnolence is often overlook in clinical settings - which can prevent remission of the mood disorder in addition to significantly interfering with quality of life. Clinical guidelines are lacking to support clinicians in the diagnosis and treatment of hypersomnolence in depression. Pharmacological treatment with selective serotonin reuptake inhibitors is insufficient and noradrenaline and dopamine reuptake inhibitors or similar molecules are generally indicated. Low-sodium oxybate was recently approved for Idiopathic Hypersomnia, but studies are needed to assess its efficacy in patients with comorbid depression. In parallel, cognitive behavioral therapy for hypersomnia is being developed as adjunct non-pharmacological treatment. Light therapy might also be beneficial in these populations. This narrative review aims at proposing a diagnostic approach reconciliating psychiatry and sleep medicine nosologies, as well as offering a multimodal treatment algorithm for hypersomnolence in depression.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 0.001 |
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".