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Record W2771966364 · doi:10.1016/j.msard.2017.12.007

The validity and reliability of screening measures for depression and anxiety disorders in multiple sclerosis

2017· article· en· W2771966364 on OpenAlexafffund
Ruth Ann Marrie, Lixia Zhang, Lisa M. Lix, Lesley A. Graff, John R. Walker, John D. Fisk, Scott B. Patten, Carol Hitchon, James M. Bolton, Jitender Sareen, Renée El‐Gabalawy, James Marriott, Çharles N. Bernstein

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of CalgaryNova Scotia Health AuthorityDalhousie UniversityUniversity of Manitoba
FundersCanadian Institutes of Health ResearchCrohn's and Colitis CanadaHeart and Stroke Foundation of Canada
KeywordsMultiple sclerosisMedicineAnxietyDepression (economics)Reliability (semiconductor)Clinical psychologyPsychiatryValidityPsychometrics

Abstract

fetched live from OpenAlex

OBJECTIVE: We aimed to evaluate the validity and reliability of multiple screening measures for depression and anxiety for use in the clinical care of people with multiple sclerosis (MS). METHODS: Participants with MS completed the Patient Health Questionnaire (PHQ-9), Hospital Anxiety and Depression Scale (HADS), Kessler-6 Distress Scale, PROMIS Emotional Distress Depression Short-Form 8a (PROMIS Depression) and Anxiety Short-Form 8a (PROMIS Anxiety), Generalized Anxiety Disorder 7-item Scale (GAD-7), and the Overall Anxiety and Severity Impairment Scale (OASIS). A subgroup repeated the screening measures two weeks later. All participants also completed a Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID). For the screening measures we computed sensitivity, specificity, positive predictive and negative predictive value with SCID diagnoses as the reference standard and conducted receiver operating curve (ROC) analyses; we also assessed internal consistency and test-retest reliability. RESULTS: Of 253 participants, the SCID classified 10.3% with major depression and 14.6% with generalized anxiety disorder. Among the depression measures, the PHQ-9 had the highest sensitivity (84%). Specificity was generally higher than sensitivity, and was highest for the HADS-D with a cut-point of 11 (95%). In ROC analyses the area under the curve (AUC) did not differ between depression measures. Among the anxiety measures, sensitivity was highest for the HADS-A with a cut-point of 8 (82%). Specificity ranged from 83% to 86% for all measures except the HADS-A with a cut-point of 8 (68%). The AUC did not differ between anxiety measures. CONCLUSION: Overall, performance of the depression and anxiety screening measures was very similar, with reasonable psychometric properties for the MS population, suggesting that other factors such as accessibility and ease of use could guide the choice of measure in clinical practice.

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)0.013
metaresearch head score (Gemma)0.037
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 score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.075
GPT teacher head0.294
Teacher spread0.219 · 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

Citations166
Published2017
Admission routes2
Has abstractyes

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