Maternal and perinatal outcomes and pharmacological management of Covid-19 infection in pregnancy: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Over 4.2 million confirmed cases and more than 285,000 deaths, COVID-19 pandemic continues to harm significant number of people worldwide. Several studies have reported the impact of COVID-19 in general population; however, there is scarcity of information related to pharmacological management and maternal and perinatal outcomes during the pandemic. Altered physiological, anatomical, and immunological response during pregnancy makes it more susceptible to infections. Furthermore, during pregnancy, a woman undergoes multiple interactions with the health care system that increases her chance of getting infected; therefore, managing pregnant population presents a unique challenge. RESEARCH QUESTIONS: This systematic review seeks to answer the following questions in relation to COVID-19: What are the different clinical characteristics presented in maternal and perinatal population? What are the different maternal and perinatal outcome measures reported? What are the distinct therapeutic interventions reported to treat COVID-19? Is it safe to use "medications" used in the treatment of COVID-19 during antenatal, perinatal, postnatal, and breastfeeding? METHOD: The search will follow a comprehensive, sequential three step search strategy. Several databases relevant to COVID-19 and its impact on pregnancy including Medline, CINAHL, and LitCovid will be searched from the inception of the disease until the completion of data collection. The quality of this search strategy will be assessed using Peer Review of Electronic Search Strategies Evidence-Based Checklist (PRESS EBC). An eligibility form will be developed for a transparent screening and inclusion/exclusion of studies. All studies will be sent to RefWorks, and abstraction will be independently performed by two researchers. Risk of bias will be assessed using Cochrane Risk of Bias tool for randomized controlled trials, Newcastle-Ottawa Quality Assessment Scale for non-randomized studies, and for case reports, Murad et al. tool will be used. Decision to conduct meta-analysis will be based on several factors including homogeneity and outcome measures reported; otherwise, a narrative synthesis will be deemed appropriate. DISCUSSION: This systematic review will summarize the existing data on effect of COVID-19 on maternal and perinatal population. Furthermore, to the best of our knowledge, this is the first systematic review addressing therapeutic management and safety of medicines to treat COVID-19 during pregnancy and breastfeeding. SYSTEMATIC REVIEW REGISTRATION: This systematic review has been registered and published with Prospero ( CRD42020172773 ).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".