Influence of innate cytokine production capacity on clinical manifestation and severity of pediatric meningococcal disease
Bibliographic record
Abstract
OBJECTIVE: To analyze the role of the innate production capacity for tumor necrosis factor, interleukin-1beta, interleukin-12, and interleukin-10 in the clinical presentation and severity of meningococcal disease. DESIGN: Whole blood cultures from survivors of severe meningococcal disease obtained median 5.4 yrs after hospitalization were stimulated with meningococcal lipopolysaccharide and heat-killed Neisseria meningitidis bacteria. SETTING: Intensive care unit in academic hospital. PATIENTS: A total of 111 children were included. We classified these patients according to clinical manifestation in four groups: shock (n = 43); both shock and meningitis (n = 11); bacteremia (neither shock nor meningitis, n = 24); and distinct meningitis (n = 33). INTERVENTIONS: : None. MEASUREMENTS AND MAIN RESULTS: The classification into four groups stratifies these patients according to disease severity. No differences in whole blood cytokine production were found between the patients in these four groups. However, within the group of patients who had presented with shock, interleukin-1beta and the interleukin-1beta/interleukin-10 ratio were negatively correlated with disease severity (R = -.35, p = .03 and R = -.33, p = .04, respectively; Pediatric Risk of Mortality score). CONCLUSIONS: Clinical manifestation of meningococcal disease cannot be explained by the innate production capacity of whole blood cultures for the cytokines tumor necrosis factor, interleukin-1beta, interleukin-10, and interleukin-12. In patients who presented with shock, a low production capacity for interleukin-1beta and a low interleukin-1beta/interleukin-10 production ratio was associated with more severe disease.
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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 | 0.000 | 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".