Impact of Corticosteroid Treatment and Antiviral Therapy on Clinical Outcomes in Hematopoietic Cell Transplant Patients Infected with Influenza Virus
Bibliographic record
Abstract
The impact of cytokines induced during influenza infection has been described, but the effect of corticosteroids on clinical outcomes is unclear. Although antiviral therapy has been well studied in immunocompetent subjects, few data exist on its clinical efficacy in immunocompromised populations. Data from 143 hematopoietic cell transplant recipients with documented seasonal influenza infection were reviewed to examine the impact of different corticosteroid regimens and antiviral therapy on clinical outcomes. In multivariable analyses, there was no observed difference between patients who received no, low doses (<1 mg/kg/day), or high doses (≥ 1 mg/kg/day) of corticosteroids with regard to the development of lower respiratory tract disease (LRD), hypoxemia, need for mechanical ventilation, or death. However, treatment with high-dose steroids was associated with a trend toward prolonged viral shedding (odds ratio [OR], 3.3; 95% confidence interval [CI], 1.0-11; P = .05). In multivariable analyses, antiviral therapy initiated to treat upper respiratory tract infection (URI) was associated with fewer cases of LRD (OR, 0.04; 95% CI, 0-0.2; P < .01) and fewer hypoxemia episodes (OR, 0.3; 95% CI, 0.1-0.9; P = .03). Our results suggest that corticosteroids are not associated with adverse clinical outcomes in hematopoietic cell transplant recipients infected with influenza, although use of higher doses may delay viral clearance. Antiviral therapy initiated during the URI phase reduced the risk of LRD and hypoxemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".