Impact of SARS-CoV-2 (COVID-19) on pregnancy: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
Introduction The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has been growing at an accelerating rate, and has become a public health emergency. Pregnant women and their fetuses are susceptible to viral infection, and outcomes in this population need to be investigated. Methods and analysis PubMed, Web of Science, Embase, CINAHAL, Latin American and Caribbean Health Sciences Literature, clinicaltrials.gov, SCOPUS, Google Scholar and Cochrane Central Controlled Trials Registry will be searched for observational studies (cohort and control cases) published from December 2019 to present. This systematic review and meta-analysis will include studies of pregnant women at any gestational stage diagnosed with COVID-19. The primary outcomes will be maternal and foetal morbidity and mortality. Three independent reviewers will select the studies and extract data from the original publications. The risk of bias will be assessed using the Newcastle-Ottawa Scale for observational studies. To evaluate the strength of evidence from the included data, we will use Grading of Recommendation Assessment, Development, and Evaluation method. Data synthesis will be performed using Review Manager software V.5.2.3. To assess heterogeneity, we will compute the I 2 statistics. Additionally, a quantitative synthesis will be performed if the included studies are sufficiently homogenous. Ethics and dissemination This study will be a review of the published data, and thus it is not necessary to obtain ethical approval. The findings of this systematic review will be published in a peer-reviewed journal. PROSPERO registration number PROSPERO 2020: CRD42020181519.
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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 | 0.066 | 0.086 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.024 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.006 |
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".