Pregnancy and Childbirth in COVID-19 Positive/Probable and Suspected Patients: A Comprehensive Review
Bibliographic record
Abstract
Corona virus disease 2019 (COVID-19) is a global pandemic disease caused by novel corona virus called SARS-CoV-2. Over 213 countries as of July 15, 2020, 13.1 million people are affected by this deadly virus. More than 100 million women are pregnant worldwide and potentially all are at risk of exposure to SARS-CoV-2 infection. Coronaviruses cause illness ranging in severity from common cold and severe respiratory illness to death. Frequent manifestations of COVID-19 include fever, cough, myalgia, headache, and diarrhoea. Abnormal test result shows abnormalities on chest radiographic imaging, lymphopenia, leukopenia, and thrombocytopenia. Physiological changes during pregnancy like altered immunity, reduced functional residual volume, pressure on diaphragm by advanced gravid uterus may lead to adverse respiratory outcome in any viral disease. Maternal mortality was very high in other corona viruses like Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). No evidence of in utero transmission was seen in SARS or MERS. Coronavirus disease 2019 might increase the risk of vertical transmission and pregnancy complications. So, meticulous management is necessary for safe maternal and foetal outcome. Early isolation, aggressive infection control procedures, oxygen therapy are the key component of COVID-19 management. In pregnancy multidisciplinary approach should be taken for general and obstetrical management. At present there is no specific treatment for COVID-19. Based on results from observational studies empiric antibacterial and antiviral drugs are used. Very recently a few controlled trials were published that suggest few treatment options. On the basis of published data and recommendations of international health organizations, the aim of this review is to explore effective treatment and care of the pregnant women throughout pregnancy, during childbirth and afterwards in this novel SARS-CoV-2 crisis. J Bangladesh Coll Phys Surg 2020; 38(0): 91-108
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".