A Primed Neutrophil Subset Predicts the Risk of Bloodstream Infections in Allogeneic Hematopoietic Stem-Cell Transplant Patients: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: Bloodstream infections (BSIs) are the most common infectious complication in patients who receive allogeneic hematopoietic stem-cell transplants (allo-HSCTs). Polymorphonuclear neutrophils (PMNs) are quantified to monitor the susceptibility to BSIs; however, their degree of activation is not. We previously identified a population of primed PMNs (pPMNs) with distinct markers of activation representing approximately 10% of PMNs in circulation. In this study, we investigate whether susceptibility to BSIs is related to the proportion of pPMNs rather than strictly PMN counts. METHODS: In this prospective observational study, we used flow cytometry to assess pPMNs in blood and oral rinse samples collected from patients receiving an allo-HSCT over the course of their treatment. We used the proportion of pPMNs in the blood on day 5 post-transplant to categorize patients into a high- or a low-pPMN group (>10% or <10% pPMNs). These groups were then used as a predictor of BSIs. RESULTS: A total of 76 patients were enrolled in the study with 36 in the high-pPMN group and 40 in the low-pPMN group. Patients in the low-pPMN group had lower expression of PMN activation and recruitment markers and displayed a delay in PMN repopulation of the oral cavity after the transplant. These patients were more susceptible to BSIs compared with patients in the high-pPMN group with an odds ratio of 6.5 (95% confidence interval, 2.110-25.07; P = .002). CONCLUSIONS: In patients who receive an allo-HSCT, having <10% pPMNs early in the post-transplant phase can be an independent predictor of BSI in allo-HSCT 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".