The Maternal and Perinatal Outcomes of Pregnancies Complicated by Covid-19: A Retrospective Study at a Tertiary Facility in Guyana (Georgetown Public Hospital Corporation)
Bibliographic record
Abstract
Background: In December 2019, there was an outbreak of pneumonia cases in the city of Wuhan, China. The outbreak was first manifested as a cluster of mysterious pneumonia cases mostly related to the Huanan Seafood market in Wuhan, the capital of Hubei Province. Genomic investigations of the pathogen found that the disease was caused by a new strain of coronaviruses and the disease was given the name “Covid 19”. Pregnant women with Covid-19 may be at greater risk of severe illness although the full impact of the infection in pregnancy is unknown. Amongst the Guyanese population, several pregnant mothers have tested positive to Covid-19 thereby generating major concerns. Objectives: To ascertain the maternal, perinatal and immediate neonatal outcomes of pregnancies complicated by Covid-19 infection at the Georgetown Public Hospital Corporation, Guyana. Method: This study is a retrospective chart review of pregnant patients who were diagnosed with Covid-19 from August, 2020 to April 2021 and delivered at GPHC. The data collected include socio-demographics of patients, parity, gestational age at delivery, birth weight amongst others. Results: This retrospective study was able to show the outcomes of Covid-19 in pregnant patients at the Georgetown Public Hospital from August, 2020 to April, 2021. During this study period, the total study population of this research was 4,686 of which only seventy-nine (79) participants fit the study criteria. Conclusion: This retrospective study shows there was no statistically significant impact seen in the maternal and neonatal outcomes of pregnancies complicated by Covid-19 infection at Georgetown Public Hospital Corporation (GPHC). Keywords: Maternal and Perinatal Outcomes of Pregnancies, Covid-19, Georgetown Public Hospital Corporation
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".