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Record W2136168452 · doi:10.2190/l811-0738-10ng-7157

Self-Reported Allergies and Their Relationship to Several Axis I Disorders in a Community Sample

2007· article· en· W2136168452 on OpenAlexaffabout
Scott B. Patten, Jeanne V.A. Williams

Bibliographic record

VenueThe International Journal of Psychiatry in Medicine · 2007
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAllergySample (material)MedicineDermatologyEnvironmental healthPsychologyImmunologyChemistryChromatography

Abstract

fetched live from OpenAlex

OBJECTIVE: Several community studies have identified associations between allergies and depressive symptoms. In this study, we evaluated the association between self-reported allergies and several Axis I disorders in a community population. METHOD: The data source was the 2002 Canadian Community Health Study. This study included the Composite International Diagnostic Interview, and collected self-report data about food and environmental allergies. Crude associations were estimated and logistic regression was subsequently used to adjust for demographic variables. RESULTS: Self-reported allergies to food and non-food allergies were associated with mood and anxiety disorders, but not to substance dependence. The adjusted odds ratio for major depression in subjects reporting food allergies was 1.8 (95% CI 1.5-2.3) and for other allergies was 1.5 (95% CI 1.2-1.7). Associations of comparable strength were observed for bipolar disorder and for panic disorder/agoraphobia. The association with social phobia was statistically significant, but not as strong. CONCLUSIONS: Cross-sectional epidemiological data are most useful for descriptive purposes. This study is the first to confirm the presence of an association between allergies and mood and anxiety disorders, as opposed to symptom ratings, in a general population sample. Substance use disorders are not associated with self-reported allergies.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.092
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.322
Teacher spread0.289 · 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

Citations83
Published2007
Admission routes2
Has abstractyes

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