Successful treatment of prolonged COVID-19 with remdesivir and nirmatrelvir/ritonavir in a patient with a history of diffuse large B-cell lymphoma: a case report
Bibliographic record
Abstract
BACKGROUND: Immunocompromised individuals, such as those affected by and treated for hematological malignancies, face a higher risk of prolonged SARS-CoV-2 infection. Increased disease risk is further compounded by limited treatment options. Currently, approved antiviral monotherapies against COVID-19 include remdesivir (Veklury) and nirmatrelvir/ritonavir (Paxlovid) which have stringent recommended prescribing windows within 7 and 5 days of symptom onset, respectively. Furthermore, these two antiviral therapies are approved for treatment lengths of 3 (remdesivir) and 5 days (Paxlovid). CASE PRESENTATION: Herein, we describe the successful treatment of prolonged COVID-19 in a patient with a history of diffuse large B-cell lymphoma with an extended combination therapy; remdesivir and nirmatrelvir/ritonavir. The patient presented with symptomatic COVID-19 that was unsuccessfully treated with a 10-day course of remdesivir. After 2 months of symptomatic infection, the patient was treated with remdesivir in combination with nirmatrelvir/ritonavir for 10 days, which quickly resolved the cough and cleared viral load. CONCLUSION: Our case highlights the efficacy of administrating a combination treatment of remdesivir and nirmatrelvir/ritonavir outside recommended guidelines for the treatment of persistent COVID-19 infection in an immunocompromised individual. High-quality studies evaluating the usefulness of this combinatory therapy as a longer-course treatment in patients with neoplasms is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".