Maternal Deaths caused by COVID-19 Infection in the First Year of the Pandemic Wave
Bibliographic record
Abstract
Highlights: These cases of maternal deaths caused by COVID-19 infections illustrated the significant risk factors for maternal mortality during the early phases of the pandemic, while studies had not extensively reported this. COVID-19 infections increase the risk of maternal and neonatal mortality, with infants having a lower chance of survival even if they are delivered. Respiratory support, antiviral medications, antibiotics, anticoagulants, and supportive care are the primary treatments for severe COVID-19 in pregnancy. AbstractThis article presents seven cases of maternal deaths attributed to COVID-19 during the first year of the pandemic wave. These cases provide insights into the natural progression of COVID-19 in pregnant women who were not vaccinated. This study showed that COVID-19 significantly increased maternal and neonatal mortality and morbidity. All of the patients exhibited symptoms of fever, cough, and dyspnea upon admission to the hospital. They were admitted with elevated respiratory rates (26–32 times/minute) and low oxygen saturation (<95%). Four patients had obesity, while one patient had pregestational diabetes. The COVID-19 diagnosis was established using a rapid antibody or antigen test and chest X-ray, which indicated pneumonia. Medical interventions administered to the patients included antiviral therapy (5 patients), antibiotics (6 patients), and anticoagulants (4 patients). From a total of five babies delivered, four babies were delivered via cesarean section. Two babies were not delivered due to previability and maternal deaths before delivery. The patients passed away within 3–10 days of hospital admission. In conclusion, adequate and early intervention and management of pregnant women infected with COVID-19 are crucial in preventing maternal and neonatal deaths, especially in unvaccinated women.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".