Bacteremia in solid organ transplant recipients as compared to immunocompetent patients: Acute phase cytokines and outcomes in a prospective, matched cohort study
Bibliographic record
Abstract
We undertook a prospective, matched cohort study of patients with Staphylococcus aureus bacteremia (SAB) and gram-negative bacteremia (GNB) to compare the characteristics, outcomes, and chemokine and cytokine response in transplant recipients to immunocompetent, nontransplant recipients. Fifty-five transplant recipients (GNB n = 29; SAB n = 26) and 225 nontransplant recipients (GNB n = 114; SAB n = 111) were included for clinical analysis. Transplant GNB had a significantly lower incidence of septic shock than nontransplant GNB (10.3% vs 30.7%, p = .03). Thirty-day mortality did not differ significantly between transplant and nontransplant recipients with GNB (10.3% vs 15.8%, p = .57) or SAB (0.0% vs 11.7%, p = .13). Next, transplant patients were matched 1:1 with nontransplant patients for the chemokine and cytokine analysis. Five cytokines and chemokines were significantly lower in transplant GNB vs nontransplant GNB: IL-2 (median [IQR]: 7.1 pg/ml [7.1, 7.1] vs 32.6 pg/ml [7.1, 88.0]; p = .001), MIP-1β (30.7 pg/ml [30.7, 30.7] vs 243.3 pg/ml [30.7, 344.4]; p = .001), IL-8 (32.0 pg/ml [5.6, 53.1] vs 59.1 pg/ml [39.2, 119.4]; p = .003), IL-15 (12.0 pg/ml [12.0, 12.0] vs 12.0 pg/ml [12.0, 126.7]; p = .03), and IFN-α (5.1 pg/mL [5.1, 5.1] vs 5.1 pg/ml [5.1, 26.3]; p = .04). Regulated upon Activation, Normal T Cell Expressed and Secreted (RANTES) was higher in transplant SAB vs nontransplant SAB (mean [SD]: 750.2 pg/ml [194.6] vs 656.5 pg/ml [147.6]; p = .046).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".