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Record W2015053071 · doi:10.1089/cap.2005.15.589

Emerging Treatments for Child and Adolescent Social Phobia: AReview

2005· review· en· W2015053071 on OpenAlexaff
Catherine Mancini, Michael Van Ameringen, Mark Bennett, Beth Patterson, Chris Watson

Bibliographic record

VenueJournal of Child and Adolescent Psychopharmacology · 2005
Typereview
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsPsychologyPsychotherapistSpecific phobiaChild and adolescent psychiatryClinical psychologyPsychiatryAnxietyAnxiety disorder

Abstract

fetched live from OpenAlex

Social anxiety disorder, or social phobia (SP), is an anxiety disorder characterized by excessive fear of exposure to situations that involve potential scrutiny by others. SP is a common psychiatric problem in children and adolescents, often presenting with comorbid anxiety and mood disorders. Although the onset of SP is typically in late childhood or early adolescence, most afflicted individuals go undiagnosed for years, not seeking treatment until adulthood. First-line treatments for SP in adults support the use of pharmacotherapy and cognitive behavioral therapy. There is new and emerging data in youths with SP to support the use of similar treatments. This paper will review the clinical characteristics, epidemiology, and treatment of SP in youths. Current investigations using selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors, benzodiazepines, and monoamine oxidase inhibitors in youths will be reviewed. Several studies on the use of cognitive behavioral therapy in youths will also be examined. Practical guidelines for clinicians who treat children and adolescents are also presented.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.042
GPT teacher head0.389
Teacher spread0.346 · 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 designSystematic review
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

Citations61
Published2005
Admission routes1
Has abstractyes

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