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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".