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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".