Hematologic characteristics and coagulopathy in pregnancy with COVID-19 succeeding the first wave: a multicenter retrospective cross-sectional study
Bibliographic record
Abstract
Background: Early reports have demonstrated an association of COVID-19 infection during pregnancy and postpartum period with coagulopathy and bleeding complications and indicated that pregnant people with COVID-19 are more likely to experience coagulopathy and venous thromboembolism. A recent report concerning such complications during the first wave of the pandemic was reassuring; however, no publications have evaluated these issues in the context of increased illness severity with the emergence of SARS-CoV-2 variants of concern. Objectives: We performed a retrospective, multinational cohort study in Canada, Romania, and the United Kingdom, aiming to provide a comprehensive analysis of the hematologic test characteristics of pregnancies affected by COVID-19 after the first wave of the pandemic. Results: Three-hundred-seventy patients were evaluated. Markers of inflammation and endothelial dysfunction were significantly elevated, in keeping with observations in the nonpregnant population. Reassuringly, despite more severe disease noted in succeeding waves of the pandemic, there was no significant evidence of COVID-19-associated coagulopathy, and overall, no association was demonstrated between isolated coagulation abnormalities and bleeding risk. Notably, fibrinogen below 2g/L was again linked with the risk of postpartum hemorrhage. Finally, venous thromboembolism risk was low but noted more frequently in those with severe illness despite thromboprophylaxis. Conclusion: Our findings add valuable insights into the nature of hematologic test characteristics, bleeding, and thrombotic complications for those affected with COVID-19 in pregnancy, reassuring readers of the low incidence of bleeding and thrombotic complications but inviting further debate as to the degree of thromboprophylaxis that may benefit the subgroup with severe disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".