COVID-19 and Other Viral Infections in Patients With Hematologic Malignancies
Bibliographic record
Abstract
COVID-19 and our armamentarium of strategies to combat it have evolved dramatically since the virus first emerged in late 2019. Vaccination remains the primary strategy to prevent severe illness, although the protective effect can vary in patients with hematologic malignancy. Strategies such as additional vaccine doses and now bivalent boosters can contribute to increased immune response, especially in the face of evolving viral variants. Because of these new variants, no approved monoclonal antibodies are available for pre-exposure or postexposure prophylaxis. Patients with symptomatic, mild-to-moderate COVID-19 and risk features for developing severe COVID-19, who present within 5-7 days of symptom onset, should be offered outpatient therapy with nirmatrelvir/ritonavir (NR) or in some cases with intravenous (IV) remdesivir. NR interacts with many blood cancer treatments, and reviewing drug interactions is essential. Patients with severe COVID-19 should be managed with IV remdesivir, tocilizumab (or an alternate interleukin-6 receptor blocker), or baricitinib, as indicated based on the severity of illness. Dexamethasone can be considered on an individual basis, weighing oxygen requirements and patients' underlying disease and their perceived ability to clear infection. Finally, as CD19-targeted and B-cell maturation (BCMA)-targeted chimeric antigen receptor (CAR) T-cell therapies become more heavily used for relapsed/refractory hematologic malignancies, viral infections including COVID-19 are increasingly recognized as common complications, but data on risk factors and prophylaxis in this patient population are scarce. We summarize the available evidence regarding viral infections after CAR T-cell therapy.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".