Evolution of the affective state of a cohort of people suffering from long covid and associated factors
Bibliographic record
Abstract
Introduction Long COVID patients have experienced a decline in their quality of life caused, in part but not wholly, by its negative emotional impact. Some of the most prevalent mental symptoms presented by Long COVID patients are anxiety, depression and sleep disorders. Objectives The objective of this study is to increase understanding of the affective state of people diagnosed with Long COVID, the evolution and associated factors. Methods Longitudinal study of three months of duration. The study population was 100 post-COVID-19 patients aged 18 years or older (80 women and 20 men). The main variable was the affective state through the Hospital Anxiety and Depression Scale (HADS) questionnaire. The rest of the collected variables were: Socio-demographic variables, number of residual symptoms, cognitive functioning using the Montreal Cognitive Assessment (MoCA), physical functioning variable measured by Sit to Stand Test and Sleep quality through the Insomnia Severity Index (ISI). A statistical analysis comparing baseline and 3months follow up measures were performed, using a Student T for related samples statistical. A lineal regression analysing associated factors to a reduction in HADS score was also performed. Ethics approval was granted by the Clinical Research Ethics Committee of Aragón (PI21/139 and PI21/454). Results At baseline the score in anxiety, depression and total score were 9,10 (SD: 4,67), 8,25 (SD: 4,51) and 17,35 (SD: 8,43) respectively, and 74% of the participants were considered cases. At three months, there is a slightly decrease but not significative in the score of HADS, both in anxiety, depression and total score (pvalue 0,465; 0,236; and 0,216 respectively). 64,4% of the participants had a positive diagnosis of depression/anxiety. About the rest of the variables there were also a slight decrease but without being significant There was not a predictive model that explained the decrease in the HADS score. Conclusions The evolution of the people suffering long covid is very slow along the time, and also the affective state. Disclosure of Interest None Declared
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".