Defining a long COVID ‘expotype’ within the P4O2 COVID-19 study
Bibliographic record
Abstract
INTRODUCTION: Long COVID is estimated to affect at least 10 % of COVID-19 patients, with fatigue being a common complaint. The combined contribution of environmental factors (i.e. exposome) has been associated with COVID-19 severity, however its association with long COVID remains underexplored. This study aims to identify possible exposome phenotypes ('expotypes') related to long COVID severity. METHODS: We recruited 95 long COVID patients in the Netherlands and assessed a range of factors and symptoms at 3-6 months post-infection. Fatigue (FSS), Quality of Life (QoL) and fatigue over time were used as indicators of long COVID severity. We included air pollutants (n = 4), and neighborhood characteristics (n = 7). We performed frequentist and Bayesian analyses to determine factors associated with long COVID severity. Models were adjusted for age, BMI, education level, and sex. RESULTS: We found population density (odds ratio (OR)[95 %Confidence interval(CI)] = 1.03[1.01-1.06]) and light at night (OR[95 %CI] = 0.95[0.90-1.00]) to be associated with fatigue. Decreased odds for having an optimal QoL score was found for increased distance to blue space (OR[95 %CI] = 0.41[0.15-0.93]) in the single exposure model. No significant associations were found for any exposure variables and fatigue over time. No exposure variables were selected in penalized regression models for any outcome. DISCUSSION: The external exposome could be associated with fatigue severity and QoL in long COVID patients, however these associations were not found in the horseshoe model. Prevention strategies and urban planning could take these associations into account to optimize the living environment, however more research is needed to validate and investigate the impact of these results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".