Low Counts of B Cells, Natural Killer Cells, Monocytes, Dendritic Cells, Basophils, and Eosinophils are Associated with Postengraftment Infections after Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Hematopoietic cell transplant (HCT) recipients are immunocompromised and thus predisposed to infections. We set out to determine the deficiency of which immune cell subset(s) may predispose to postengraftment infections. We determined day 28, 56, 84, and 180 blood counts of multiple immune cell subsets in 219 allogeneic transplant recipients conditioned with busulfan, fludarabine, and Thymoglobulin. Deficiency of a subset was considered to be associated with infections if the low subset count was significantly associated with subsequent high infection rate per multivariate analysis in both discovery and validation cohorts. Low counts of monocytes (total and inflammatory) and basophils, and low IgA levels were associated with viral infections. Low plasmacytoid dendritic cell (PDC) counts were associated with bacterial infections. Low inflammatory monocyte counts were associated with fungal infections. Low counts of total and naive B cells, total and CD56(high) natural killer (NK) cells, total and inflammatory monocytes, myeloid dendritic cells (MDCs), PDCs, basophils and eosinophils, and low levels of IgA were associated with any infections (due to any pathogen or presumed). In conclusion, deficiencies of B cells, NK cells, monocytes, MDCs, PDCs, basophils, eosinophils, and/or IgA plasma cells appear to predispose to postengraftment infections.
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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 | 0.000 |
| 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) | 0.001 | 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".