Post-viral fatigue following SARS-CoV-2 infection during pregnancy: a longitudinal comparative study
Bibliographic record
Abstract
Abstract Background: Several studies have reported post-COVID-19 fatigue in the general population, but none explored post-COVID-19 fatigue among pregnant women. The objectives of this study were to determine the prevalence over time, duration and risk factors of post-viral fatigue among pregnant women infected with SARS-CoV-2. Methods: Longitudinal comparative study involving 588 pregnant women with SARS-CoV-2 investigation during pregnancy or at delivery in Sao Paulo, Brazil. Three groups were investigated: G1 (N=259) women with COVID-19 (symptomatic SARS-CoV-2 infection) identified during pregnancy; G2 (N=131) women with positive SARS-CoV-2 serology determined at delivery; G3 (N=198) women with negative SARS-CoV-2 serology at delivery. Questionnaires investigating fatigue were applied at 6 weeks, 3 and 6 months after SARS-CoV-2 identification for G1; and at delivery, 6 weeks, 3 and 6 months after delivery for all groups. The prevalence of fatigue, fatigue most of the time, and significant fatigue were determined at all timepoints. Cox regression analysis was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) to evaluate the risk of remaining with fatigue over time in G1 women.Results: Prevalence of overall fatigue in G1 women at 6 weeks, 3 and 6 months were 40.6%, 33.6% and 27.8%, respectively. The cumulative risk of remaining with fatigue increased over time according to the severity of disease, 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. In multivariate analysis, the independent risk factors of fatigue in G1 women were cough and myalgia. At and after delivery the prevalence of fatigue was significantly higher in G1 women compared to G2 and G3 women at all time points. Conclusions: The prevalence of post-viral fatigue is higher in pregnant women acquiring SARS-CoV-2 during pregnancy, and the risk and duration of fatigue increase with 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".