COVID-19 Lockdown Increased the Risk of Preterm Birth
Bibliographic record
Abstract
Purpose: To estimate whether the city-specific lockdown in Shanghai induced by the COVID-19 pandemic affected preterm birth rates among uninfected pregnant women in different trimesters. Methods: The population-based retrospective cohort study was conducted in the International Peace Maternity and Child Health Hospital (IPMCH) in Shanghai, China. Pregnant women without COVID-19 received perinatal healthcare during lockdown (from January 24, 2020 to March 24, 2020) and non-lockdown (from January 24, 2019 to March 24, 2019) period and giving birth to a live infant at IPMCH were enrolled. 1:1 propensity score matching and Inverse probability of treatment weighting were used to evaluate preterm birth (<37 weeks), very preterm birth (<34 weeks), preterm birth with premature rupture of membranes (PROM-PTB), spontaneous preterm birth with intact membranes (S-PTB), and medically induced preterm birth (MI-PTB) between two groups. Results: 8,270 pregnant women were in the lockdown group, and 9,815 were in the non-lockdown group. Pregnant women in second trimester during lockdown had a higher risk of PTB than those during the non-lockdown period [OR: 1.43 (CI 1.01–2.02), ARD: 1.7% (CI 0.04–3.4%), p = 0.045]. Furthermore, pregnant women in third trimester during lockdown had a higher risk of PROM-PTB than those during the non-lockdown period [OR: 1.64 (CI 1.09–2.47), ARD: 0.9% (CI 0.2–1.6%), p = 0.02]; no group differences were found related to rates of VPTB, S-PTB or MI-PTB. Conclusion: In this cohort study in China, we found that there was an increased risk in preterm birth for non-infected women in COVID-19 lockdown who were in their second trimester.
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How this classification was reachedexpand
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".