COVID-19 Pandemic: Different Associative Relationships of City Lockdown With Preterm Births in Three Cities – An Ecological Study
Bibliographic record
Abstract
In 2020, the global spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection (also known as COVID-19) has led to pandemic health issues with significant changes in individual and community practices. Preterm birth could be one of the risks in pregnant mothers who are infected by the SARS-CoV-2. Preterm births contribute upto 10% of all births and incur significant impact on the child health and cost to the health care system. However, the association of city lockdown during COVID-19 pandemic with the rate of preterm births is unclear. In a cohort study, we examined the association of city lockdown during the COVID-19 pandemic with the births at different gestations in three different cities. Compared with the pre-pandemic epoch, the associative relationships ranged from a decrease in all births, all births across all preterm gestations and to preterm births in moderately and late preterm gestations. We concluded that there were variable associative relationships of city lockdown during COVID-19 pandemic with preterm births. This could be related to the differences in health, societal and cultural factors, which will inspire further studies in this area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".