0807 Central Disorders of Hypersomnolence in Major Depressive Disorder
Bibliographic record
Abstract
Abstract Introduction Central disorders of hypersomnia (CDH), characterized by excessive daytime sleepiness, frequently overlap with major depressive disorder (MDD), complicating diagnosis and treatment. Understanding the prevalence of CDH in MDD is crucial for enhancing diagnostic accuracy and optimizing treatment strategies. This systematic review and meta-analysis aimed to estimate the pooled prevalence of CDH in MDD patients and explore variations based on demographic, geographic, and methodological factors. Methods A comprehensive search of PubMed, Embase, Cochrane Library, PsycINFO, and CINAHL was conducted to identify observational studies reporting CDH prevalence in MDD patients. Eligible studies were assessed for quality using the Newcastle-Ottawa Scale. Data were extracted systematically, and a random-effects model was employed to calculate pooled prevalence and explore heterogeneity. Subgroup analyses were conducted by age, gender, diagnostic criteria, geographic region, and study type. Publication bias was evaluated using funnel plots and Egger’s test, with trim-and-fill adjustments applied. Results Twelve studies met inclusion criteria, comprising a total of 71,633 MDD patients. The pooled prevalence of CDH was 20.23% (95% CI: 7.31%–44.93%), increasing to 30.17% (95% CI: 18.24%–42.09%) after accounting for publication bias. Subgroup analyses revealed the highest prevalence among adolescents (34.2%, 95% CI: 24.1%–44.3%), followed by adults (23.5%, 95% CI: 15.0%–32.0%), and older adults (10.3%, 95% CI: 4.5%–16.1%). Gender-based analyses indicated slightly higher prevalence among males (31.8%, 95% CI: 19.2%–47.8%) compared to females (30.5%, 95% CI: 15.9%–50.6%), although overlapping confidence intervals suggest these differences are not statistically significant. Geographic variations showed the highest rates in Asia (24.7%, 95% CI: 16.4%–33.0%) and Europe (21.8%, 95% CI: 13.1%–30.5%). Diagnostic criteria analysis revealed slightly higher prevalence in studies using DSM-V (22.7%, 95% CI: 14.1%–31.3%) compared to DSM-IV (19.5%, 95% CI: 12.0%–27.0%). Funnel plots indicated potential publication bias, and sensitivity analyses confirmed the robustness of the findings. Conclusion CDH is highly prevalent in MDD patients, with significant variations across demographic and methodological subgroups. These findings underscore the importance of routine sleep assessments in MDD management and highlight the need for integrated diagnostic and treatment approaches. Future research should focus on elucidating causal relationships and addressing gaps in underrepresented populations to improve care for patients with comorbid CDH and MDD. Support (if any)
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".