PS-C30-12: IMPACT OF SARS-COV-2 INFECTION TO RISK AND SEVERITY OF HYPERTENSION IN PREGNANCY AND ITS OUTCOME TO THE NEW BORN
Bibliographic record
Abstract
Objective: To determine relationship of SARS-CoV-2 infection to the risk and severity of preeclampsia, as well as its impact on newborns. Design and method: We performed a systematic search in databases (PubMed, ScienceDirect, ProQuest, and Cochrane Library) for studies examining impact of SARS-CoV-2 infection on pregnancy. Included studies were evaluated for risk of bias based on the Newcastle Ottawa Score. A meta-analysis was conducted using the data extracted from each study. Review Manager (RevMan) 5.4 was utilized to compute the summary of odds ratios (OR), mean differences (MD), and 95% confidence intervals (CI) for the outcomes. Our outcomes of interest are preeclampsia, preeclampsia with severe features, eclampsia, fetal distress and still birth. The other outcomes are preterm birth (< 37 week), instrumental labor, sectio caesaria and birth defect. Results: We identified twenty two observational studies involving 1,025,048 pregnancy patients. Based on the analysis, SARS-CoV-2 infection in pregnancy significantly increased the risk of preeclampsia [OR 2.01(95% CI 1.59–2.53; p < 0.00001; I2 = 82%)], and the severity was based on the high prevalence of preeclampsia with severe features [OR 3.04(95% CI 1.19–7.78; p = 0.02; I2 = 91%)] and eclampsia [OR 17.73(95% CI 13.83–22.72;p < 0.00001; I2 = 0%)]. Poor outcome in newborns in terms of incidence of preterm birth [OR 1.65(95% CI 1.54–1.76; p < 0.00001; I2 = 86%)], fetal distress [OR 19.18(95% CI 17.14–21.45; p < 0.00001; I2 = 99%)] and still birth [OR 2.12(95% CI 1.74–2.59; p < 0.00001; I2 = 0%)], were also significantly associated with SARS-CoV-2 infection. Conclusions: SARS-CoV-2 infection during pregnancy increases the risk and severity of preeclampsia and gives a poor outcome in newborn.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".