Respiratory Viruses in Patients With Hematological Malignancy in Boreal Autumn/Winter 2023–2024: <scp>EPICOVIDEHA</scp>‐<scp>EPIFLUEHA</scp> Report
Bibliographic record
Abstract
Community-acquired respiratory viral infections (CARV) significantly impact patients with hematological malignancies (HM), leading to high morbidity and mortality. However, large-scale, real-world data on CARV in these patients is limited. This study analyzed data from the EPICOVIDEHA-EPIFLUEHA registry, focusing on patients with HM diagnosed with CARV during the 2023-2024 autumn-winter season. The study assessed epidemiology, clinical characteristics, risk factors, and outcomes. The study examined 1312 patients with HM diagnosed with CARV during the 2023-2024 autumn-winter season. Of these, 59.5% required hospitalization, with 13.5% needing ICU admission. The overall mortality rate was 10.6%, varying by virus: parainfluenza (21.3%), influenza (8.8%), metapneumovirus (7.1%), RSV (5.9%), or SARS-CoV-2 (5.0%). Poor outcomes were significantly associated with smoking history, severe lymphopenia, secondary bacterial infections, and ICU admission. This study highlights the severe risk CARV poses to patients with HM, especially those undergoing active treatment. The high rates of hospitalization and mortality stress the need for better prevention, early diagnosis, and targeted therapies. Given the severe outcomes with certain viruses like parainfluenza, tailored strategies are crucial to improving patient outcomes in future CARV seasons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".