MétaCan
Menu
Back to cohort
Record W2191801283 · doi:10.3109/02770903.2015.1104694

Condition-specific associations of symptoms of depression and anxiety in adolescents and young adults with asthma and food allergy

2015· article· en· W2191801283 on OpenAlexafffund
Mark A. Ferro, Ryan J. Van Lieshout, James G. Scott, Rosa Alati, Abdullah Al Mamun, Kaeleen Dingle

Bibliographic record

VenueJournal of Asthma · 2015
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster University
FundersNational Health and Medical Research CouncilMedical Research CouncilHamilton Health Sciences
KeywordsAsthmaAnxietyMedicineDepression (economics)Food allergyAllergyPsychiatryYoung adultMental healthClinical psychologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This study examined associations of asthma and food allergy with symptoms of depression and anxiety at 14 and 21 years of age to determine whether condition-specific associations exist. METHODS: Data come from 4972 adolescents in the Mater University Study of Pregnancy. Symptoms of depression and anxiety were assessed using the Youth Self-Report and Young Adult Self-Report. RESULTS: Condition-specific associations between asthma and depression, OR = 1.37 [1.12, 1.67] and between food allergy and anxiety, OR = 1.26 [1.04, 1.76] were found during adolescence, but not in young adulthood. Whereas asthma was associated with resolved depression, OR = 1.70 [1.13, 2.55], food allergy was associated with persistent anxiety, OR = 1.26 [1.01, 1.59]. CONCLUSIONS: In adolescents, asthma is associated with an increased risk of clinically relevant symptoms of depression and food allergy with an increased risk of clinically relevant symptoms of anxiety. Future research is needed to clarify directionality and mechanisms explaining these relationships. Health professionals should be aware of the increased risk of mental health problems in adolescents with asthma or food allergy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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 teacher head, 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

Citations58
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of AsthmaSame topicAsthma and respiratory diseasesFrench-language works237,207