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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".