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Record W4399531955 · doi:10.1080/03007995.2024.2360647

Association between fatigue and depressive symptoms in persons with post-COVID-19 condition: a post hoc analysis

2024· article· en· W4399531955 on OpenAlexafffund
Kayla M. Teopiz, Angela T.H. Kwan, Gia Han Le, Ziji Guo, Sebastian Badulescu, Felicia Ceban, Shakila Meshkat, Joshua D. Di Vincenzo, Giacomo d’Andrea, Bing Cao, Roger Ho, Taeho Greg Rhee, Donovan A. Dev, Lee Phan, Mehala Subramaniapillai, Rodrigo B. Mansur, Joshua D. Rosenblat, Roger S. McIntyre

Bibliographic record

VenueCurrent Medical Research and Opinion · 2024
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsMcMaster UniversityUniversity of TorontoUniversity of OttawaUniversity Health NetworkBrain and Cognition Discovery Foundation
FundersBrain and Cognition Discovery Foundation
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Depressive symptomsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPost-hoc analysisDepression (economics)Association (psychology)PsychiatryInternal medicineDiseaseAnxietyVirologyOutbreak

Abstract

fetched live from OpenAlex

Objective Post-COVID-19 Condition (PCC) is a prevalent, persistent and debilitating phenomenon occurring three or more months after resolution of acute COVID-19 infection. Fatigue and depressive symptoms are commonly reported in PCC. We aimed to further characterize PCC by assessing the relationship between fatigue and depressive symptom severity in adults with PCC.Methods A post hoc analysis was conducted on data retrieved from a randomized, double-blinded, placebo-controlled study evaluating vortioxetine for cognitive deficits in persons with PCC. We sought to determine the relationship between baseline fatigue [i.e. Fatigue Severity Scale (FSS) total score] and baseline depressive symptom severity [i.e. 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR-16) total score] in adults with PCC.Results The statistical analysis included baseline data from 142 participants. After adjusting for age, sex, education, employment status, history of major depressive disorder (MDD) diagnosis, self-reported physical activity, history of documented acute SARS-CoV-2 infection and body mass index (BMI), baseline FSS was significantly correlated with baseline QIDS-SR-16 (β = 0.825, p = .001)Conclusion In our sample, baseline measures of fatigue and depressive symptoms are correlated in persons living with PCC. Individuals presenting with PCC and fatigue should be screened for the presence and severity of depressive symptoms. Guideline-concordant care should be prescribed for individuals experiencing clinically significant depressive symptoms. Fatigue and depressive symptom severity scores were not pre-specified as primary objectives of the study. Multiple confounding factors (i.e. disturbance in sleep, anthropometrics and cognitive impairment) were not collected nor adjusted for in the analysis herein. Trial registration Unrestricted Research Grant from H. Lundbeck A/S, Copenhagen, Denmark. ClinicalTrials.gov Identifier: NCT05047952

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)0.003
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.055
GPT teacher head0.448
Teacher spread0.392 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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