Bibliographic record
Abstract
Background: Intestinal infections are frequently occur among children with cancer who receive chemotherapy. On the other hand, diarrhea is especially common and severe among cancer patients that develop neutropenia, either due to the disease itself or due to the intensive chemotherapy. There are many causes of diarrhea among those patients, but intestinal infections still an important etiology among them. Objectives: to study the frequency of diarrhea among neutropenic children, with its infectious etiologies, especially the bacterial, fungal and parasitic causes. Type of the study:Cross-sectional study. Methods: the study was done in the Oncology Department of Nanakali Hospital for Haematological diseases and malignanciesin Erbil City, on pediatric age group. One hundred six children with cancer were followed up during the period between January – May 2017, of them only 50 patients who full fill the criteria of being (neutropenic, diarrheic, and age < 14 years), and those were regarded as the study group, compared to 20 patients who had the same criteria (diarrheic, and age < 14 years) but notneutropenic.They were investigated for the infectious causes of diarrhea especially bacterial, parasitic, and fungal agents. Data were analyzed statistically using SPSS program and Correlation test was also used. The results were regarded significant with p < 0.05. Results: A total of 70 diarrheal episodes in 106 cancer children were detected, 50 of them were neutropenic while 20 were not. Intestinal infections were detected in 62% of the 1st group and in 45% of the 2nd one, while the causes in the remaining cases of diarrhea (38%) cannot be identified. Bacterial pathogens were the main agents that causes diarrhea followed by fungi then parasites with an infectious rates of (28%, 20%, and 14% respectively). Conclusions: Diarrhea commonly developed among cancer children with neutropenia. Bacteria are the most incriminated pathogens followed by Candida and parasites. This study noticed the presence of other etiologies for diarrhea beside infectious causes that should be considered and investigated in the future researches and during management of diarrhea in those patients.
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 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 | 0.001 |
| 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".