Association between fatigue and depressive symptoms in persons with post-COVID-19 condition: a post hoc analysis
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".