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Record W2012226386 · doi:10.1007/s12160-012-9389-y

COPD and Depressive Symptoms: Findings from the Guangzhou Biobank Cohort Study

2012· article· en· W2012226386 on OpenAlexaff
Adrian Loerbroks, Chao Qiang Jiang, G. Neil Thomas, Peymané Adab, Wei Sen Zhang, Jos A. Bosch, Kar Keung Cheng

Bibliographic record

VenueAnnals of Behavioral Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsInnovation Cluster (Canada)
FundersGuangzhou Municipal Science and Technology BureauUniversity of Hong KongUniversity of Birmingham
KeywordsMedicineCOPDSpirometryDepression (economics)Airway obstructionCohortInternal medicineBiobankCohort studyPopulationCenter for Epidemiologic Studies Depression ScaleAirwayDepressive symptomsPhysical therapyPsychiatrySurgeryAsthmaBioinformaticsAnxiety

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic obstructive pulmonary disease (COPD) co-exists with depression, but important questions remain about the determinants of this association. PURPOSE: We examined the association of depressive symptoms with three aspects of COPD: self-reports of physician-diagnosed COPD, chronic respiratory symptoms, and airway obstruction. METHODS: We used data from the Guangzhou Biobank Cohort Study (n = 7,995). Airway obstruction was assessed by spirometry. A score ≥4 on the 15-item Geriatric Depression Scale was used as a cutoff for depressive symptoms. RESULTS: Self-reported COPD was positively associated with depressive symptoms but airway obstruction was not. Compared to those free of both respiratory symptoms and airway obstruction those reporting respiratory symptoms were more likely to have depressive symptoms regardless of whether they had obstruction or not. CONCLUSIONS: In this Chinese population, a self-reported physician diagnosis of COPD and symptom perception, but not airway obstruction, appeared as main determinants of depressive symptoms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.079
GPT teacher head0.400
Teacher spread0.321 · 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.

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

Citations6
Published2012
Admission routes1
Has abstractyes

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