Impact of Lockdown Measures during COVID-19 Pandemic on Pregnancy and Preterm Birth
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to assess the effect of the lockdown measures during the coronavirus disease 2019 (COVID-19) pandemic on pregnancy outcomes of women who were not affected by severe acute respiratory syndrome coronavirus 2 infection. STUDY DESIGN: We used data from the perinatal health program and neonatal databases to conduct a cohort analysis of pregnancy outcomes during the COVID-19 lockdown in the Calgary region, Canada. Rates of preterm birth were compared between the lockdown period (March 16 to June 15, 2020) and the corresponding pre-COVID period of 2015 to 2019. We also compared maternal and neonatal characteristics of preterm infants admitted to neonatal intensive care units (NICUs) in Calgary between the two periods. FINDINGS: = 0.71). During the lockdown period, the likelihood of multiple births was lower (risk ratio [RR] 0.73, 95% confidence interval [CI]: 0.60-0.88), while gestational hypertension and clinical chorioamnionitis increased (RR 1.24, 95%CI: 1.10-1.40; RR 1.33, 95%CI 1.10-1.61, respectively). CONCLUSION: Observed rates of very preterm and very-low-birth-weight births decreased during the COVID-19 lockdown. Pregnant women who delivered during the lockdown period were diagnosed with gestational hypertension and chorioamnionitis more frequently than mothers in the corresponding pre-COVID period. KEY POINTS: · Lockdown measures to reduce COVID-19 transmission were associated with a lower rate of preterm birth.. · Mental and physical wellbeing of pregnant women were significantly affected by the lockdown measures.. · A comprehensive public health plan to relieve psychosocial stress during pregnancy is required..
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".