Severe novel influenza A (H1N1) infection in cancer patients
Bibliographic record
Abstract
BACKGROUND: The natural history and consequences of severe H1N1 influenza infection among cancer patients are not yet fully characterized. We describe eight cases of H1N1 infection in cancer patients admitted to the intensive care unit of a referral cancer center. PATIENTS AND METHODS: Clinical data from all patients admitted with acute respiratory failure due to novel viral H1N1 infection were reviewed. Lung tissue was submitted for viral and bacteriological analyses by real-time RT-PCR, and autopsy was conducted on all patients who died. RESULTS: Eight patients were admitted, with ages ranging from 55 to 65 years old. There were five patients with solid organ tumors (62.5%) and three with hematological malignancies (37.5%). Five patients required mechanical ventilation and all died. Four patients had bacterial bronchopneumonia. All deaths occurred due to multiple organ failure. A milder form of lung disease was present in the three cases who survived. Lung tissue analysis was performed in all patients and showed diffuse alveolar damage in most patients. Other lung findings were necrotizing bronchiolitis or extensive hemorrhage. CONCLUSIONS: H1N1 viral infection in patients with cancer can cause severe illness, resulting in acute respiratory distress syndrome and death. More data are needed to identify predictors of unfavorable evolution in these patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".