Editorial: Maternal SARS-CoV-2 Infection and Pregnancy Outcomes from Current Global Study Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the global COVID-19 pandemic, data from clinical studies, systematic review, and population registry data have shown that when compared with non-pregnant women, SARS-CoV-2 infection in pregnancy is associated with a small increase in risk to the mother. Large cohort studies and registry data collected from 2020 have included the US Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET), COVI-PREG, the UK and Global Pregnancy and Neonatal Outcomes in COVID-19 (PAN-COVID) study, the American Academy of Pediatrics (AAP) Section on Neonatal-Perinatal Medicine (SONPM) National Perinatal COVID-19 Registry, the Swedish Pregnancy Register, and the Canadian Surveillance of COVID-19 in Pregnancy (CANCOVID-Preg) registry. Recently published data have shown that most maternal infections with SARS-CoV-2 occur during the third trimester and result in a small increase in hospital admission, admission to the intensive care unit (ICU), mechanical ventilation, preterm birth, and increased cesarean sections in mothers infected with SARS-CoV-2. However, currently approved vaccines given in pregnancy result in an immune response to current SARS-CoV-2 variants. Transplacental transmission of SARS-CoV-2 to the fetus can occur, but the immediate and long-term effects on the newborn infant remain unclear. Therefore, women who are pregnant or planning a pregnancy should be managed according to current clinical guidelines with timely vaccination to prevent infection with SARS-CoV-2. This Editorial summarizes what is currently known about maternal SARS-CoV-2 infection and pregnancy outcomes from multinational studies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it