Association between lymphocyte subsets and cytomegalovirus infection status among patients with systemic lupus erythematosus
Bibliographic record
Abstract
This study aimed to determine the association between different lymphocyte subsets and cytomegalovirus (CMV) infection status in patients with systemic lupus erythematosus (SLE). We performed a retrospective study among SLE patients with CMV infection and collected patient socio-demographic and clinical characteristics, as well as their recorded circulating lymphocyte subsets. Univariate and multivariable logistic regression analyses examined the relationship between CMV infection status and lymphocyte subset counts. We included 125 hospitalized patients with SLE, consisting of 88 with documented CMV infection and 37 without any evidence of CMV or other infections. Among the 88 CMV-infected patients, 65 (73.8%) patients developed CMV disease and 23 (26.2%) presented as CMV viremia. Compared to uninfected patients (1520 ± 101 cells/μL), lymphocytes remained stable among those with CMV viremia (1305 ± 272 cells/μL, P = .995). However, compared to their uninfected counterparts, there was a marked decrease in lymphocytes among patients with CMV disease (680 ± 513 cells/μL, P < .001). Analysis of lymphocyte subsets via flow cytometry showed that CD4+ T cell, CD8+ T cell, and natural killer cell counts were lower among those with CMV disease compared to those with CMV viremia and those without infection. Further, multivariable analysis showed that total lymphocyte (odds ratio [OR] 0.999, 95% confidence interval [CI] 0.998-1.000, P = .007) and CD4+ T cell counts (OR 0.99, 95% CI 0.992-0.998, P = .003) were negatively associated with CMV disease. Our findings support a potential inverse relationship between lymphopenia, specifically CD4+ T-cell lymphopenia, and CMV disease among hospitalized SLE patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".