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Record W2778030950 · doi:10.1097/mph.0000000000001065

Clinical Presentation, Prognostic Factors, and Outcome in Neutropenic Enteropathy of Childhood Leukemia

2017· article· en· W2778030950 on OpenAlexaff
İdil Rana Üser, Sinan Akbayram, Bülent Hayrı Özokutan

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicinePneumatosis intestinalisInternal medicineAcute myeloblastic leukemiaSepsisHypoalbuminemiaComplicationSurgeryLeukemiaGastroenterology

Abstract

fetched live from OpenAlex

Leukemia patients are at risk for neutropenic enteropathy (NEP) because of the effects of intensified chemotherapy. Medical records of 18 patients having 20 episodes of NEP were reviewed retrospectively. Primary diagnosis was acute lymphoblastic leukemia in 12 and myeloblastic leukemia in 6 cases. According to prognosis, 3 patients were in the standard-risk group, 6 in the moderate-risk group, and 9 in the high-risk group. Ultrasonography detected increased bowel wall thickness in 6 patients. Abdominal x-ray revealed air-fluid levels (n=8), pneumatosis intestinalis, pneumoperitoneum (n=1), and portal venous gas (n=1). All patients received medical treatment, and 1 with unrelieved hematochezia required resection of the cecum. Two cases with appendicitis and another 1 with pneumoperitoneum responded to antibiotics and recovered without surgery. The mortality rate was 30% and related to sepsis-induced complications. The presence of hypokalemia, hypoalbuminemia, metabolic acidosis, and admission to the intensive care unit were more common in patients with mortality (P=0.01). In conclusion, NEP should be kept in mind as a treatable but potentially lethal complication of childhood leukemia. Radiologic findings should be interpreted in conjunction with clinical picture. A conservative approach should be used in all cases but surgery can be considered in some situations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.410
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2017
Admission routes1
Has abstractyes

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