Predictors of Viridans Streptococcal Shock Syndrome in Bacteremic Children With Cancer and Stem-Cell Transplant Recipients
Bibliographic record
Abstract
Purpose To describe episodes of viridans streptococcal bacteremia (VSB) in a cohort of children with cancer and stem-cell transplant (SCT) recipients and to determine predictors of viridans streptococcal shock syndrome (VSSS) in this group of children. Patients and Methods For this retrospective review, we included episodes of VSB isolated between March 1997 and September 2002, in children (≤ 18 years) with a diagnosis of cancer or SCT patients. The primary outcome was VSSS, defined as hypotension requiring intravascular volume expansion or inotropic support and/or respiratory insufficiency necessitating assisted ventilation. Results Eighty-eight episodes of VSB occurred in 79 children. The mean age of the children was 6.7 years (range, 0.6 to 18.0 years). The most common underlying diagnosis was acute myelogenous leukemia (AML) in 31 (35%) of 88 episodes, and 38 (43%) of 88 had undergone SCT. VSSS occurred in 16 (18%) of 88 episodes, and two children died from VSSS. Two variables were predictive of VSSS, namely peak temperature at presentation (odds ratio [OR], 6.3; 95% CI, 2.1 to 19.0; P = .001) and inpatient status (OR, 5.9; 95% CI, 1.3 to 28.0; P = .02). Diagnosis of AML (OR, 1.1; 95% CI, 0.4 to 3.5; P = .8), receipt of SCT (OR, 1.9; 95% CI, 0.6 to 5.7; P = .2), high-dose cytarabine (OR, 0.6; 95% CI, 0.1 to 3.2; P = .6), and mucositis (OR, 0.8; 95% CI, 0.3 to 2.6; P = .7) were not predictive of VSSS. Conclusion VSSS occurred in 18% of episodes of VSB in children with cancer or SCT recipients. Peak temperature before antibiotic therapy and inpatient status were predictive of VSSS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".