SARS-CoV-2 and Placental Pathology
Bibliographic record
Abstract
The extent to which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection at different points in the pregnancy timeline may affect maternal and fetal outcomes remains unknown. We sought to characterize the impact of SARS-CoV-2 infection proximate and remote from delivery on placental pathology. We performed a secondary analysis of placental pathology from a prospective cohort of universally tested SARS-CoV-2 positive women >20 weeks gestation at 1 institution. Subjects were categorized as having acute or nonacute SARS-CoV-2 based on infection <14 or ≥14 days from delivery admission, respectively, determined by nasopharyngeal swab, symptom history, and serologies, when available. A subset of SARS-CoV-2 negative women represented negative controls. Placental pathology was available for 90/97 (92.8%) of SARS-CoV-2 positive women, of which 26 were from women with acute SARS-CoV-2 infection and 64 were from women with nonacute SARS-CoV-2. Fetal vascular malperfusion lesions were significantly more frequent among the acute SARS-CoV-2 group compared with the nonacute SARS-CoV-2 group (53.8% vs. 18.8%; P=0.002), while frequency of maternal vascular malperfusion lesions did not differ by timing of infection (30.8% vs. 29.7%; P>0.99). When including 188 SARS-CoV-2 negative placentas, significant differences in frequency of fetal vascular malperfusion lesions remained between acute, nonacute and control cases (53.8% vs. 18.8% vs. 13.2%, respectively; P<0.001). No differences were noted in obstetric or neonatal outcomes between acutely and nonacutely infected women. Our findings indicate timing of infection in relation to delivery may alter placental pathology, with potential clinical implications for risk of thromboembolic events and impact on fetal health.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".