Factors influencing predisposition to sepsis in children with cancers and acquired immunodeficiencies unrelated to human immunodeficiency virus infection
Bibliographic record
Abstract
OBJECTIVES: The main objectives of this review are to provide insight into the various factors that affect the risk of sepsis in immunocompromised children and to discuss special issues that should be considered when such patients are enrolled in clinical trials. STRATEGY: A literature review was conducted, and authoritative references were consulted when appropriate. This was supported by discussion among experts at an international consensus conference on pediatric sepsis. OUTCOME: The review discusses general issues as they relate to the factors that are associated with a predisposition to sepsis in children with cancers and non-human immunodeficiency virus (HIV)-related acquired immunodeficiencies. The host defects that are associated with specific infections are discussed, and an overview of the indicators of immune dysfunction in the previously well child is presented. Selected examples of patients with non-HIV-related acquired immunodeficiencies, including those with cancer or who have undergone solid-organ or hematopoietic stem-cell transplants are discussed. Special challenges that may affect clinical trials include the altered immune response as this relates to the definition of infection and disease and the assessment of outcomes and the heterogeneity of study populations due to the variable manifestations of immune deficiency states. SUMMARY: Knowledge of the factors that are associated with sepsis in immunocompromised patients is important when such patients are to be entered into clinical trials on sepsis. These factors do not necessarily operate in isolation and may occur concurrently or sequentially. With these considerations in mind, clinical trials involving immunocompromised children can go forward and will very likely lead to significant advances in the care of this understudied population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".