Post-Viral Fatigue Following SARS-CoV-2 Infection during Pregnancy: A Longitudinal Comparative Study
Bibliographic record
Abstract
Studies reported post-COVID-19 fatigue in the general population, but not among pregnant women. Our objectives were to determine prevalence, duration, and risk factors of post-viral fatigue among pregnant women with SARS-CoV-2. This study involved 588 pregnant women with SARS-CoV-2 during pregnancy or delivery in Brazil. Three groups were investigated: G1 (n = 259, symptomatic infection during pregnancy); G2 (n = 131, positive serology at delivery); G3 (n = 198, negative serology at delivery). We applied questionnaires investigating fatigue at determined timepoints after infection for G1, and after delivery for all groups; fatigue prevalence was then determined. Cox regression was used to estimate hazard ratio (HR) and 95% CI of the risk of remaining with fatigue in G1. Overall fatigue prevalence in G1 at six weeks, three months and six months were 40.6%, 33.6%, and 27.8%, respectively. Cumulative risk of remaining with fatigue increased over time, with HR of 1.69 (95% CI: 0.89–3.20) and 2.43 (95% CI: 1.49–3.95) for women with moderate and severe symptoms, respectively. Multivariate analysis showed cough and myalgia as independent risk factors in G1. Fatigue prevalence was significantly higher in G1 compared to G2 and G3. Post-viral fatigue prevalence is higher in women infected during pregnancy; fatigue’s risk and duration increased with the severity of infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".