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Record W110673145 · doi:10.1177/070674370605100610

Characterization and Treatment Response of Anxious Children with Asthma

2006· article· en· W110673145 on OpenAlexaffvenue
Tripti Papneja, Katharina Manassis

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2006
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsAnxietyAsthmaPsychosocialDepression (economics)Anxiety disorderClinical psychologyStressorMedicinePsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare children with Axis I anxiety disorders and asthma with a matched group of anxious children without asthma on questionnaire measures and response to cognitive-behavioural treatment (CBT) for anxiety. METHOD: A sample of 36 children with comorbid anxiety and asthma, aged 8 to 12 years, were matched for age, sex, and specific anxiety disorder with 36 children with an Axis I anxiety disorder but no asthma. Parents and children completed standardized questionnaires. RESULTS: Children with comorbid anxiety and asthma had significantly more perinatal complications (P = 0.001), and higher total (P = 0.000) and psychological stressors (P = 0.02), especially parent-child problems (P = 0.01), but lower levels of depression (P = 0.03) and anxiety (P = 0.05), compared with anxious, nonasthmatic children. All children reported decreased anxiety (P = 0.001) and depression (P = 0.000) posttreatment, with a trend toward less improvement in anxiety in anxious children with asthma. CONCLUSIONS: Although replication is needed, addressing psychosocial stress and parent-child problems may increase CBT efficacy in children with comorbid anxiety and asthma.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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

Citations26
Published2006
Admission routes2
Has abstractyes

Explore more

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