A Description of the Type, Frequency and Severity of Infections Among Sixteen Patients Treated for T-Cell Lymphoma
Bibliographic record
Abstract
BACKGROUND: Infections are an important cause of morbidity and mortality in T-cell lymphomas. Factors contributing to increased risk of infection include the nature of the underlying disease, as well as treatment-associated immunosuppression. Currently there are few reports describing the types of infections, including preventable infections, in this cohort of patients. The aim of the study was to identify the type, frequency and severity of infection in patients with T-cell lymphoma undergoing treatment. METHODS: A case series was performed on all patients with T-cell lymphoma over a 5-year period from 2011 to 2016 at a tertiary Australian hospital. Information was collected from medical record review regarding patient demographics, lymphoma treatment and outcomes, and infectious outcomes. Severe infections were recorded, defined as infection requiring hospitalization. RESULTS: , with the most common source of infection being skin and soft tissue. There was one case of cytomegalovirus (CMV) infection and five cases (12%) of invasive fungal infection. The highest rates of infection occurred during progressive disease. Rates of prophylaxis were highest with antiviral agents, and comparatively lower with antibacterial and antifungal agents. CONCLUSION: Infections are frequent, opportunistic and severe in patients with T-cell lymphoma. Our data suggests that fungal prophylaxis may be indicated with T-cell lymphoma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".