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The Evolution of Juvenile Myelomonocytic Leukemia in a Female Patient with Paternally Inherited Neurofibromatosis Type 1

2003· article· en· W2332566461 on OpenAlexaff
Elaine Leung, Wilma Vanek, Mohamed Abdelhaleem, Melvin H. Freedman, Yigal Dror

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2003
Typearticle
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsJuvenile myelomonocytic leukemiaMedicineNeurofibromatosisNeurofibromatosis type IGirlNeurofibromin 1MalignancyLeukemiaDiseaseMyeloidImmunologyInternal medicineCancer researchHaematopoiesisPathologyGeneticsBiologyStem cell

Abstract

fetched live from OpenAlex

The most common myeloid malignancy seen in children with neurofibromatosis type 1 (NF-1) is juvenile myelomonocytic leukemia (JMML), a myeloproliferative disease. The vast majority of these children have inherited the neurocutaneous disease from an affected mother; boys are more often affected than girls. We present the rare finding of a 7-year-old girl with NF-1 who developed JMML. She inherited her NF-1 from the father. At the time of her initial presentation, clonogenic assays of bone marrow mononuclear cells did not show the spontaneous growth of granulocyte-macrophage colony-forming units or hypersensitivity to granulocyte-macrophage colony-stimulating factor that is characteristic of this disorder. After 1 month, repeat evaluations of the patient's clinical and laboratory test results became fully consistent with those for a diagnosis of JMML. This illustrates the stepwise evolution of this myeloproliferative disorder in NF-1 and the importance of close follow-up and reassessment of these patients. Our case is only the second report of JMML in a girl who inherited NF-1 from her father.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations6
Published2003
Admission routes1
Has abstractyes

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Same venueJournal of Pediatric Hematology/OncologySame topicNeurofibromatosis and Schwannoma CasesFrench-language works237,207