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Record W4225392379 · doi:10.1089/cap.2021.0137

Citalopram Did Not Significantly Improve Anxiety in Children with Autism Spectrum Disorder Undergoing Treatment for Core Symptoms: Secondary Analysis of a Trial to Reduce Repetitive Behaviors

2022· article· en· W4225392379 on OpenAlexaff
Emily Simonoff, Florence D. Mowlem, Oliver P. Pearson, Evdokia Anagnostou, Craig L. Donnelly, Eric Hollander, Bryan H. King, James T. McCracken, Lawrence David Scahill, Linmarie Sikich, Andrew Pickles

Bibliographic record

VenueJournal of Child and Adolescent Psychopharmacology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
FundersNational Institute of Mental HealthKing's College LondonDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsAnxietyCitalopramPlaceboAutism spectrum disorderRandomized controlled trialPsychologyAutismClinical psychologyPsychiatryPopulationAnxiety disorderAdverse effectConfidence intervalMedicineInternal medicineAntidepressant

Abstract

fetched live from OpenAlex

Objective: Anxiety disorders are among the most common co-occurring conditions in autism spectrum disorder (ASD). Despite their prevalence and impact, there are no randomized controlled trials (RCTs) aimed at evaluating the efficacy of selective serotonin reuptake inhibitors (SSRIs) for anxiolysis in this population, who may have a different biological basis for anxiety. Methods: Secondary analyses of the STAART double-blind, placebo-controlled RCT of citalopram in children with ASD examined whether citalopram reduced anxiety measured on the parent-reported Child and Adolescent Symptom Inventory-4 (CASI-4) as the primary outcome. An intention-to-treat analysis involving all 149 participants used multiple imputations for missing data and included baseline stratification factors of age group and site, among others. We prespecified as clinically significant a 33% reduction in anxiety in citalopram versus placebo, coinciding with 80% power. We tested whether communicative ability on the Vineland Communication score moderated treatment effect and explored whether initial anxiety was associated with greater adverse events, which could impact on dose titration and achieving optimal dose. Results: Both groups showed substantial reduction in anxiety. Citalopram was associated with a nonsignificant 16.5% greater reduction (observed coefficient = −0.181, bootstrap standard error = 0.126, p = 0.151, confidence interval = −0.428 to 0.066). Anxiety reports were significantly lower in children with reduced communicative ability, but communicative ability did not moderate the treatment effect (interaction p = 0.294). Initial anxiety levels were not associated with increased adverse effects (interaction ps 0.162–0.954). Conclusion: Citalopram did not statistically significantly improve anxiety in children with ASD. Clinicians should be cautious in their use of SSRIs for this indication. There remains a need for well-powered clinical trials testing the efficacy of SSRIs among autistic children with anxiety disorders.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.324
Teacher spread0.306 · 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 designNon-randomized trial
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

Citations12
Published2022
Admission routes1
Has abstractyes

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