0481 Higher Risk Of OSA Is Associated With Poorer Affect Regulation Among The Severely Mentally Ill With A History Of Aggression: Evidence From A Forensic Setting
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a sleep-related breathing disorder with negative consequences on daytime functioning, including fatigue, irritability, and poorer memory. OSA is highly prevalent among individuals diagnosed with severe mental illnesses (SMI), likely due to an accumulation of risk factors (e.g., antipsychotics, obesity, smoking). Psychiatric patients in forensic settings display greater impulsivity and emotional dysregulation compared to non-forensic psychiatric patients. We hypothesized that forensic patients at higher risk of OSA will display greater impulsivity, aggression and emotional dysregulation than forensic patients at low risk of OSA. Study participants were male (n=55) and female (n=10) forensic patients (mean age = 38.5 ±11.5). All were diagnosed with at least one SMI (e.g., schizophrenia, bipolar disorder). Participants completed the Sleep Disorders Questionnaire 2 - Sleep Apnea subscale (SDQ-2-SA) to estimate risk of OSA, followed by other self-report surveys, including: Pittsburgh Sleep Quality Inventory (PSQI), Epworth Sleepiness Scale (ESS), Buss-Perry Aggression Questionnaire (BPAQ), Barratt Impulsiveness Scale (BIS), Depression Anxiety and Stress Scale (DASS), Difficulties in Emotion Regulation Scale (DERS). Correlation analyses were run to examine the relationship between SDQ-2-SA scores and the scores from the surveys measuring different aspects of affect as well as sleep quality and sleepiness. Higher risk of OSA (as per the SDQ-2-SA) was positively correlated with poorer sleep (PSQI; r=0.34, p <.01); higher aggression (BPAQ, r=0.25, p <.01), higher impulsivity (BIS, r=0.35, p <.01), higher depression and anxiety (DASS, r=0.42, p <.001), and greater emotion dysregulation (DERS, r=0.36, p <.01). Higher risk of OSA was not correlated with sleepiness. While this remains to be replicated with objective PSG data, the present findings highlight that emotional dysregulation, impulsivity and aggression in forensic patients with various SMI may be worsened by OSA, a condition often undiagnosed and untreated among forensic patients. Future studies should investigate whether appropriate treatment in forensic patients with OSA may help improve emotional regulation, impulsivity and aggression, common features in many psychiatric disorders. UMRF
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".