Greater Role of Cognitive Impairment Over Fatigue in Post-COVID-19 Quality of Life: A Post-Hoc Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
ABSTRACT Background Post COVID-19 Condition (PCC) is a common and debilitating condition with significant reports of fatigue and psychosocial impairment globally. The extent to which cognitive symptoms and fatigue contribute to reduced quality of life in affected individuals remains clear. Methods This is a post-hoc analysis of a randomized, double-blind, placebo-controlled clinical trial that evaluated the effect of vortioxetine on cognitive function in adults with PCC. The post-hoc analysis herein aimed to determine the overall effect of baseline cognitive function [as measured by the Digit Symbol Substitution Test (DSST)] and baseline fatigue severity [as measured by the Fatigue Severity Scale (FSS)] on baseline health-related quality of life (HRQoL) [as measured by the 5-item World Health Organisation Well-Being Index (WHO-5)]. Results A total of 200 participants were enrolled in the primary trial. Due to missing baseline data, our statistical analysis included baseline measures of 147 individuals. Our generalized linear model analysis revealed a significant positive correlation between DSST-measured objective cognitive function and self-reported WHO-5-measured HRQoL (β = 0.069, 95% CI [0.006, 0.131], p = 0.032). In contrast, our analysis revealed a significant negative correlation between FSS and WHO-5 scores (β = -0.016, 95% CI [-0.021, –0.011], p < 0.001). The beta-coefficient ratio (β DSST / β FSS = 0.069 / 0.016) is calculated as 4.313. Conclusions Overall, we observed that increased cognitive function was associated with increased HRQoL at baseline in adults with PCC. Moreover, we observed that increased severity of fatigue symptoms was associated with decreased HRQoL at baseline in adults with PCC. Furthermore, we observed that an improvement in cognitive function would have a four-fold greater impact on HRQoL than the effect generated by improvement in fatigue.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".