H1N1 novel influenza A in pregnant and immunocompromised patients
Bibliographic record
Abstract
OBJECTIVE: To describe the increased risk of severe disease and the appropriate management of patients at high risk such as pregnant women and immunosuppressed patients who acquire novel influenza A (H1N1). DESIGN: Review of the literature regarding influenza A in these patient groups, and review of published and unpublished data with regard to novel influenza A (H1N1). MAIN RESULTS: Pregnant women are at increased risk for severe pneumonitis and respiratory failure from influenza infection, particularly during pandemics, including the current pandemic. Fetal morbidity is significant, usually resulting from maternal fever and severe hypoxemia. Early antiviral therapy using oseltamivir may be beneficial, and intensive care unit support should target adequate oxygenation at all times. Immunosuppressed patients are at increased risk for influenza, as well as at risk for more severe or prolonged infection. Patients after hematopoietic stem cell transplantation, after lung transplantation, and those receiving chemotherapy for leukemia are at highest risk, whereas the risk for human immunodeficiency virus-infected individuals appears relatively low. Treatment with antiviral therapy may be beneficial, even after the usual cut-off of 48 hrs after symptom onset. CONCLUSIONS: Optimal management of these patients is preventive by influenza vaccination, but the neuraminidase inhibitor antiviral agents provide effective treatment.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".