Risk management of seasonal influenza during pregnancy: current perspectives
Bibliographic record
Abstract
Mark H Yudin The Department of Obstetrics and Gynecology, St Michael's Hospital, Li Ka Shing Knowledge Institute, University of Toronto, Toronto, ON, Canada Abstract: Influenza poses unique risks to pregnant women, who are particularly susceptible to morbidity and mortality. Historically, pregnant women have been overrepresented among patients with severe illness and complications from influenza, and have been more likely to require hospitalization and intensive care unit admission. An increased risk of adverse outcomes is also present for fetuses/neonates born to women affected by influenza during pregnancy. These risks to mothers and babies have been observed during both nonpandemic and pandemic influenza seasons. During the H1N1 influenza pandemic of 2009–2010, pregnant women were more likely to be hospitalized or admitted to intensive care units, and were at higher risk of death compared to nonpregnant adults. Vaccination remains the most effective intervention to prevent severe illness, and antiviral medications are an important adjunct to ameliorate disease when it occurs. Unfortunately, despite national guidelines recommending universal vaccination for women who are pregnant during influenza season, actual vaccination rates do not achieve desired targets among pregnant women. Pregnant women are also sometimes reluctant to use antiviral medications during pregnancy. Some of the barriers to use of vaccines and medications during pregnancy are a lack of knowledge of recommendations and of safety data. By improving knowledge and understanding of influenza and vaccination recommendations, vaccine acceptance rates among pregnant women can be improved. Currently, the appropriate use of vaccination and antiviral medications is the best line of defense against influenza and its sequelae among pregnant women, and strategies to increase acceptance are crucial. This article will review the importance of influenza in pregnancy, and discuss vaccination and antiviral medications for pregnant women. Keywords: antiviral medications, knowledge, vaccination
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".