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Record W4378652311 · doi:10.1093/sleep/zsad077.0669

0669 Disruptive Nocturnal Behaviors in PTSD Patients are Associated with More Severe Psychiatric Pathology

2023· article· en· W4378652311 on OpenAlexaff
Madhulika A. Gupta, Carmelina Anello

Bibliographic record

VenueSLEEP · 2023
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsScreamingPsychologyNightmareSomatizationPsychiatryHypervigilancePolysomnographySleep disorderClinical psychologyInsomniaAnxietyElectroencephalography

Abstract

fetched live from OpenAlex

Abstract Introduction The similarities and differences between trauma associated sleep disorder (TASD) and REM sleep behavior disorder (RBD) are unclear. However, both REM sleep parasomnias are associated with disruptive nocturnal behaviors (DNB) that manifest as episodes of sleep-related vocalizations, and/or complex motor behaviors. Patients who have more DNB are more likely to have a higher prevalence of PTSD. Methods In a cross-sectional study, we examined the relationship between DNB severity and the severity of psychiatric symptoms in PTSD patients. 73 civilian patients with histories of complex developmental trauma (age 47.0 ±13.3 years; 89.6% female) met the DSM-5 criteria for moderate-to-severe PTSD. PTSD Checklist for DSM-5 (PCL-5) was used to obtain an index of current PTSD severity (37.41 ± 19.84; PCL-5 >30 cutoff for PTSD). DNB was defined as the sum of the two items 1f and 1g in the Pittsburgh Sleep Quality Index PTSD Addendum (PTSD-A). The participants rated how often during the past 1 month they had trouble sleeping because of the following: (PTSD-A Item1f) “Had episodes of terror or screaming during sleep without fully awakening” and (PTSD-A Item1g) “Had episodes of ‘acting out’ their dreams, such as kicking, punching, running or screaming”. We used a stepwise multiple regression analysis with DNB as the dependent variable and the variables described below as independent variables. Results We determined the following relationships: 1. PCL-5 (r=0.359, p=0.002); 2. Brief Symptom Inventory which includes Somatization (r=0.476,p< 0.001);Obsessive-compulsiveness (r=0.429, p< 0.001); Interpersonal Sensitivity (r=0.337, p=0.004); Depression (r=0.398, p< 0.001); Anxiety (r=0.449,p< 0.001); Hostility (r=0.577, p< 0.001); Phobic Anxiety (r=0.353, p=0.002); Paranoid Ideation (r=0.412, p< 0.001); and Psychoticism (r=0.414, p< 0.001); 3. Insomnia Severity Index (r=0.363, p=0.002); 4. Beck Suicide Scale or BSS (r=0.268, p=0.025); 5.Beck Anxiety Inventory (r=0.472, p< 0.001); and 6. Fear of Sleep(r=0.493,p< 0.001). Fear of Sleep Inventory (FOS) (Beta=0.442, t=4.258, p< 0.001) and Beck Scale for Suicidal Ideation (BSS) (Beta=0.300, t=2.887, p=0.005) emerged as the significant predictors of DNB. Conclusion DNB was correlated with a wide range of psychiatric pathology in PTSD. This suggests that DNB could be an integral feature of severe PTSD. To our knowledge, this has not been previously reported. Support (if any) None

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.009
GPT teacher head0.275
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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