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Record W2288966423 · doi:10.1002/rnj.263

Diurnal Variations in Psychological Distress in Chronic Obstructive Pulmonary Disease

2016· article· en· W2288966423 on OpenAlexafffund
Emilie Chan-Thim, Marie Dumont, Amanda Rizk, Zohra Parwanta, Véronique Pepin, Grégory Moullec

Bibliographic record

VenueRehabilitation Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversité de MontréalConcordia UniversityHôpital du Sacré-Cœur de MontréalUniversité du Québec en OutaouaisUniversité du Québec à Montréal
FundersFonds de Recherche du Québec - Santé
KeywordsPulmonary diseasePsychological distressMedicineDistressDiurnal temperature variationInternal medicineClinical psychologyPsychiatryMental healthAtmospheric sciences

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to investigate the association between depressive symptoms severity and amplitude of diurnal variations in depression symptoms in patients with chronic obstructive pulmonary disease (COPD). DESIGN: Prospective, observational proof-of-concept study. METHODS: Fourteen participants with moderate/severe COPD completed a 20-item Center for Epidemiologic Studies Depression Scale (CES-D) estimating depressive symptoms severity. Throughout one week, the four-item very short version of the CES-D was completed every day in the morning, afternoon, and evening. FINDINGS: Strong positive correlations were observed between depressive severity and the mean range of diurnal variations in positive (r = .61) and depressed affects (r = .67), somatic complaints (r = .82), and disturbed interpersonal relationships (r = .71). CONCLUSION: In COPD patients, a greater diurnal variation in depression symptoms was associated with greater depression severity. This relationship seems independent of COPD severity. CLINICAL RELEVANCE: Diurnal variation in the symptoms of depression is a new method of identifying depression severity in COPD.

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)0.001
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 score0.371
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.352
Teacher spread0.334 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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