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Record W4244805824 · doi:10.32920/14637486

Assessing depression symptoms in those with insomnia: An examination of the Beck Depression Inventory Second Edition (BDI-II)

2021· preprint· en· W4244805824 on OpenAlexaff
Colleen E. Carney, Christi S. Ulmer, Jack D. Edinger, Andrew D. Krystal, Faye Knauss

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsToronto Metropolitan University
FundersNational Institute of Mental HealthNational Sleep Foundation
KeywordsIrritabilityBeck Depression InventoryInsomniaDepression (economics)PsychologyMajor depressive disorderPsychiatryPopulationReceiver operating characteristicMultivariate analysis of varianceClinical psychologySleep disorderInternal medicineMedicineMoodAnxiety

Abstract

fetched live from OpenAlex

Background: Due to concerns about overlapping symptomatology between medical conditions and depression, the validity of the Beck Depression Inventory (BDI-II) has been assessed in various medical populations. Although Major Depressive Disorder (MDD) and Primary Insomnia (PI) share some daytime symptoms, the BDI-II has not been evaluated for use with insomnia patients. Method Participants (N = 140) were screened for the presence of insomnia using the Duke Structured Clinical Interview for Sleep Disorders (DSISD), and evaluated for diagnosis of MDD using the Structured Clinical Interview for DSM-IV-TR (SCID). Participants’ mean BDI-II item responses were compared across two groups [insomnia with or without MDD) using multivariate analysis of variance (MANOVA), and the accuracy rates of suggested clinical cutoffs for the BDI-II were evaluated using a Receiver Operating Characteristic (ROC) curve analysis. Results The insomnia with depression group had significantly higher scores on several items; however, the groups did not differ on insomnia, fatigue, concentration problems, irritability, libido, increased appetite, and thoughts relating to suicide, self-criticism and punishment items. The ROC curve analysis revealed moderate accuracy for the BDI-II’s identification of depression in those with insomnia. The suggested BDI cutoff of ≥ 17 had 81% sensitivity and 79% specificity. Use of the mild cutoff for depression (≥14) had high sensitivity (91%) but poor specificity (66%). Conclusion Several items on the BDI-II might reflect sleep disturbance symptoms rather than depression per se. The recommended BDI-II cutoffs in this population have some support but a lower cutoff could result in an overclassification of depression in insomnia patients, a documented problem in the clinical literature. Understanding which items discriminate insomnia patients without depression may help address this nosological issue.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.307
Teacher spread0.285 · 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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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