Optimizing knowledge of antiviral medications for prophylaxis and treatment of influenza during pregnancy
Bibliographic record
Abstract
Pregnant women are particularly susceptible to influenza-related morbidity and mortality and have historically been over-represented among patients with influenza, requiring hospitalization and intensive care unit admission. This has been observed during both nonpandemic and pandemic influenza seasons. During the H1N1 influenza pandemic of 2009-2010, pregnant women again had an increased likelihood of hospital or intensive care unit admission, and many deaths were documented. One identified risk factor for more severe disease was a delay in the initiation of antiviral medications. Vaccination is currently the most effective method for preventing severe influenza and its sequelae, and antiviral medications are used as an important adjunct to vaccination. Knowledge among pregnant women regarding influenza vaccine recommendations is poor, but by improving knowledge and understanding, vaccine rates can be increased. Although there are no published data examining knowledge regarding antiviral medications, one can hypothesize that knowledge is similarly low. In the current era, the appropriate use of vaccination and antiviral medications is the best defense against complications of influenza among pregnant women, and optimizing knowledge about these strategies among providers and patients alike is of paramount importance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".