Importance of neutropenia for development of invasive infections at various phases of treatment for hemato-oncological diseases in children
Bibliographic record
Abstract
INTRODUCTION: Prolonged neutropenia in patients with acute myeloid leukemia (AML), relapsed acute lymphoblastic leukemia (r-ALL), myelodysplastic syndrome (MDS), and those receiving hematopoietic stem cell transplantation (HSCT), is a well-known risk factor for infectious complications. Few data are available about the incidence and etiology of infectious episodes during the total treatment period associated with a decreased immunity. METHODS: Between January 2000 and December 2005 children diagnosed with AML, r-ALL, and MDS, and post-HSCT patients were included in the study. A retrospective review based on microbiological data was performed to describe the incidence and etiology of the infectious complications during the total treatment period. RESULTS: One hundred and thirty disease-specific patient episodes were included. Forty-two percent of 184 microbiologically proven infectious episodes were diagnosed in patients receiving chemotherapy, and 58% occurred in HSCT patients. During neutropenia, 123 (67%) infectious episodes were diagnosed; of the isolated species 83% were bacterial, 6% fungal, and 11% viral. In the period without neutropenia, 61 (33%) infectious episodes were diagnosed, with 38% bacterial, 3% fungal, and 59% viral species isolated. Of the infectious episodes diagnosed in patients treated with an HSCT, 52% (n = 55) occurred in the post-engraftment period. In contrast, in patients treated with chemotherapy, 92% of the infectious episodes were diagnosed during neutropenia. CONCLUSION: The number of proven infectious episodes in post-HSCT patients was not influenced by the presence of neutropenia, while in patients receiving chemotherapy significantly lower numbers of proven infectious episodes were diagnosed outside the neutropenic period.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".