Changes in pregnancy outcomes during the COVID-19 lockdown in Iran
Bibliographic record
Abstract
BACKGROUND: The Covid-19 pandemic response is influencing maternal and neonatal health care services especially in developing countries. However, the indirect effects of Covid-19 on pregnancy outcomes remain unknown. The aim of the present study was to compare pregnancy outcomes before and after the beginning of the Covid-19 pandemic in Iran. METHODS: We performed a retrospective analysis of the medical records of 2,503 pregnant women with singleton pregnancies, admitted to the maternity department of a women's hospital in Tehran, Iran, during the pre-Covid-19 pandemic (February 19 to April 19, 2019) and the intra-Covid- 19 pandemic (February 19 to April 19, 2020) period. RESULTS: We included 2,503 women admitted to the hospital; 1,287 (51.4 %) were admitted before the Covid-19 lockdown and 1,216 (48.6 %) during the Covid-19 lockdown. There were no significant differences in stillbirth rates (p = 0.584) or pregnancy complications (including preeclampsia, pregnancy-induced hypertension and gestational diabetes) (p = 0.115) between pregnant women in the pre- and intra-pandemic periods. However, decreases in preterm births (p = 0.001), and low birth weight (p = 0.005) were observed in the pandemic period compared to the pre-pandemic period. No significant difference in the mode of delivery, and no maternal deaths were observed during the two time periods. CONCLUSIONS: In our study we observed a decrease in preterm births and low birth weight, no change in stillbirths, and a rise in the admission rates of mothers to the ICU during the initial Covid-19 lockdown period compared to pre-Covid-19 lockdown period. Further research will be needed to devise plan for immediate post-pandemic care and future health care crises.
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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 | 0.003 |
| 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 | 0.001 |
| 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".