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Baseline Severity as a Moderator of the Waiting List–Controlled Association of Cognitive Behavioral Therapy With Symptom Change in Social Anxiety Disorder

2023· review· en· W4378783468 on OpenAlexaff
Willemijn Scholten, Adrie Seldenrijk, Adriaan W. Hoogendoorn, Renske C. Bosman, Anna Muntingh, Eirini Karyotaki, Gerhard Andersson, Thomas Berger, Per Carlbring, Tomas Furmark, Stéphane Bouchard, Philippe R. Goldin, Isabel L. Kampmann, Nexhmedin Morina, Nancy L. Kocovski, Eric Leibing, Falk Leichsenring, Timo Stolz, Anton J.L.M. van Balkom, Neeltje M. Batelaan

Bibliographic record

VenueJAMA Psychiatry · 2023
Typereview
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsWilfrid Laurier UniversityUniversité du Québec en OutaouaisCégep de l'Outaouais
Fundersnot available
KeywordsSocial anxietyCognitive behavioral therapyRandomized controlled trialAnxietyPsycINFOMeta-analysisClinical psychologyCochrane LibraryModerationMEDLINEMedicinePsychologyCognitive therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Importance: Social anxiety disorder (SAD) can be adequately treated with cognitive behavioral therapy (CBT). However, there is a large gap in knowledge on factors associated with prognosis, and it is unclear whether symptom severity predicts response to CBT for SAD. Objective: To examine baseline SAD symptom severity as a moderator of the association between CBT and symptom change in patients with SAD. Data Sources: For this systematic review and individual patient data meta-analysis (IPDMA), PubMed, PsycInfo, Embase, and the Cochrane Library were searched from January 1, 1990, to January 13, 2023. Primary search topics were social anxiety disorder, cognitive behavior therapy, and randomized controlled trial. Study Selection: Inclusion criteria were randomized clinical trials comparing CBT with being on a waiting list and using the Liebowitz Social Anxiety Scale (LSAS) in adults with a primary clinical diagnosis of SAD. Data Extraction and Synthesis: Authors of included studies were approached to provide individual-level data. Data were extracted by pairs of authors following the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline, and risk of bias was assessed using the Cochrane tool. An IPDMA was conducted using a 2-stage approach for the association of CBT with change in LSAS scores from baseline to posttreatment and for the interaction effect of baseline LSAS score by condition using random-effects models. Main Outcomes and Measures: The main outcome was the baseline to posttreatment change in symptom severity measured by the LSAS. Results: A total of 12 studies including 1246 patients with SAD (mean [SD] age, 35.3 [10.9] years; 738 [59.2%] female) were included in the meta-analysis. A waiting list-controlled association between CBT and pretreatment to posttreatment LSAS change was found (b = -20.3; 95% CI, -24.9 to -15.6; P < .001; Cohen d = -0.95; 95% CI, -1.16 to -0.73). Baseline LSAS scores moderated the differences between CBT and waiting list with respect to pretreatment to posttreatment symptom reductions (b = -0.22; 95% CI, -0.39 to -0.06; P = .009), indicating that individuals with severe symptoms had larger waiting list-controlled symptom reductions after CBT (Cohen d = -1.13 [95% CI, -1.39 to -0.88] for patients with very severe SAD; Cohen d = -0.54 [95% CI, -0.80 to -0.29] for patients with mild SAD). Conclusions and Relevance: In this systematic review and IPDMA, higher baseline SAD symptom severity was associated with greater (absolute but not relative) symptom reductions after CBT in patients with SAD. The findings contribute to personalized care by suggesting that clinicians can confidently offer CBT to individuals with severe SAD symptoms.

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)0.051
metaresearch head score (Gemma)0.126
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.051
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.126
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.041
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.070
GPT teacher head0.404
Teacher spread0.333 · 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
GenreReview

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

Citations21
Published2023
Admission routes1
Has abstractyes

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