Why do sleep disorders belong to mental disorder classifications? A network analysis of the “Sleep-Wake Disorders” section of the DSM-5
Bibliographic record
Abstract
This article proposes to investigate how Sleep disorders have been conceptualized within the DSM-5 through symptom network analysis of the diagnostic criteria of the "Sleep-Wake Disorders" section in the DSM-5. We hypothesize that the analysis of the most central symptoms will allow us to better analyze the position of Sleep disorders in Mental disorders. We thus i) extracted the symptoms of the DSM-5 diagnostic criteria of Sleep-Wake disorders, ii) built the Sleep-Wake disorder DSM-5 network representation, and iii) quantified its structure at local and global levels using classical symptom network analysis. Thirty-four different symptoms were identified among the 53 DSM-5 diagnostic criteria of the 9 main disorders of the "Sleep-Wake Disorders" section. The symptom network structure of this section showed that the most central sleep symptoms are "Daytime Sleepiness", the Insomnia symptoms group ("Insomnia initiating", "Insomnia maintaining" and "Non-restorative sleep"), and Behavioral sleep symptoms (such as "Altered oniric activity", "Ambulation", "Abnormal responsiveness"). This network analysis shown that the belonging of Sleep-Wake disorders in the DSM-5 have been associated with central sleep symptoms considered as "Mental", given their phenomenality (qualitative nature of the experience) and subjectivity (in personal mental lives). Such a symptom network analysis can serve as an organizing framework to better understand the complexity of Sleep-Wake disorders by promoting research to connect the architecture of the symptom network to relevant biological, psychological and sociocultural factors.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".