A Systematic Review of Treatment and Outcomes of Pregnant Women With COVID-19—A Call for Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Data pertaining to COVID-19 in pregnancy are limited; to better inform clinicians, we collated data from COVID-19 cases during pregnancy and summarized clinical trials enrolling this population. METHODS: We performed a systematic literature review of PubMed/MEDLINE to identify cases of COVID-19 in pregnancy or the postpartum period and associated outcomes. We then evaluated the proportion of COVID-19 clinical trials (from ClinicalTrials.gov) excluding pregnant or breastfeeding persons (both through June 29, 2020). RESULTS: We identified 11 308 published cases of COVID-19 during pregnancy. Of those reporting disease severity, 21% (416/1999) were severe/critical. Maternal and neonatal survival were reassuring (98% [10 437/10 597] and 99% [1155/1163], respectively). Neonatal disease was rare, with only 41 possible cases of infection reported in the literature. Of 2351 ongoing COVID-19 therapeutic clinical trials, 1282 were enrolling persons of reproductive age and 65% (829/1282) excluded pregnant persons. Pregnancy was an exclusion criterion for 69% (75/109) of chloroquine/hydroxychloroquine, 80% (28/35) of lopinavir/ritonavir, and 48% (44/91) of convalescent plasma studies. We identified 48 actively recruiting or completed drug trials reporting inclusion of this population. CONCLUSIONS: There are limited published reports of COVID-19 in pregnancy despite more than 14 million cases worldwide. To date, clinical outcomes appear reassuring, but data related to important long-term outcomes are missing or not yet reported. The large number of clinical trials excluding pregnant persons, despite interventions with safety data in pregnancy, is concerning. In addition to observational cohort studies, pregnancy-specific adaptive clinical trials could be designed to identify safe and effective treatments.
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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 | 0.035 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".