Epidemiology and Clinical Outcomes of Hospitalizations for Acute Respiratory or Febrile Illness and Laboratory-Confirmed Influenza Among Pregnant Women During Six Influenza Seasons, 2010–2016
Bibliographic record
Abstract
BACKGROUND: Pregnant women are at increased risk of seasonal influenza hospitalizations, but data about the epidemiology of severe influenza among pregnant women remain largely limited to pandemics. METHODS: To describe the epidemiology of hospitalizations for acute respiratory infection or febrile illness (ARFI) and influenza-associated ARFI among pregnant women, administrative and electronic health record data were analyzed from retrospective cohorts of pregnant women hospitalized with ARFI who had testing for influenza viruses by reverse-transcription polymerase chain reaction (RT-PCR) in Australia, Canada, Israel, and the United States during 2010-2016. RESULTS: Of 18 048 ARFI-coded hospitalizations, 1064 (6%) included RT-PCR testing for influenza viruses, 614 (58%) of which were influenza positive. Of 614 influenza-positive ARFI hospitalizations, 35% were in women with low socioeconomic status, 20% with underlying conditions, and 67% in their third trimesters. The median length of influenza-positive hospitalizations was 2 days (interquartile range, 1-4), 18% (95% confidence interval [CI], 15%-21%) resulted in delivery, 10% (95% CI, 8%-12%) included a pneumonia diagnosis, 5% (95% CI, 3%-6%) required intensive care, 2% (95% CI, 1%-3%) included a sepsis diagnosis, and <1% (95% CI, 0%-1%) resulted in respiratory failure. CONCLUSIONS: Our findings characterize seasonal influenza hospitalizations among pregnant women and can inform assessments of the public health and economic impact of seasonal influenza on pregnant women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".