A comprehensive review of the impact of COVID-19 on human reproductive biology, assisted reproduction care and pregnancy: a Canadian perspective
Bibliographic record
Abstract
Currently, the world is in the seventh month of the COVID-19 pandemic. Globally, infections with novel SARS-CoV-2 virus are continuously rising with mounting numbers of deaths. International and local public health responses, almost in synchrony, imposed restrictions to minimize spread of the virus, overload of health system capacity, and deficit of personal protective equipment (PPE). Although in most cases the symptoms are mild or absent, SARS-CoV-2 infection can lead to serious acute respiratory disease and multisystem failure. The research community responded to this new disease with a high level of transparency and data sharing; with the aim to better understand the origin, pathophysiology, epidemiology and clinical manifestations. The ultimate goal of this research is to develop vaccines for prevention, mitigation strategies, as well as potential therapeutics.The aim of this review is to summarize current knowledge regarding the novel SARS CoV-2, including its pathophysiology and epidemiology, as well as, what is known about the potential impact of COVID-19 on reproduction, fertility care, pregnancy and neonatal outcome. This summary also evaluates the effects of this pandemic on reproductive care and research, from Canadian perspective, and discusses future implications.In summary, reported data on pregnant women is limited, suggesting that COVID-19 symptoms and severity of the disease during pregnancy are similar to those in non-pregnant women, with pregnancy outcomes closely related to severity of maternal disease. Evidence of SARS-CoV-2 effects on gametes is limited. Human reproduction societies have issued guidelines for practice during COVID-19 pandemic that include implementation of mitigation practices and infection control protocols in fertility care units. In Canada, imposed restrictions at the beginning of the pandemic were successful in containing spread of the infection, allowing for eventual resumption of assisted reproductive treatments under new guidelines for practice. Canada dedicated funds to support COVID-19 research including a surveillance study to monitor outcomes of COVID-19 during pregnancy and assisted reproduction. Continuous evaluation of new evidence must be in place to carefully adjust recommendations on patient management during assisted reproductive technologies (ART) and in pregnancy.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".