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Record W4386280296 · doi:10.14740/jh1150

Leukostasis With Isolated Central Nervous System Involvement in Chronic Phase of Chronic Myelogenous Leukemia

2023· article· en· W4386280296 on OpenAlexvenueno aff
Won Jin Jeon, Suhani Dalal, Jin Hyun Moon, Bowon Joung, Michael Nguyen, Dani Castillo, Jessica Hudson, Kiwon Park, Ravi Raghavan, Mojtaba Akhtari, Ami B. Patel

Bibliographic record

VenueJournal of Hematology · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsLeukostasisMedicineChronic myelogenous leukemiaMyeloid leukemiaComplicationHematologyLeukemiaMidostaurinMyeloproliferative neoplasmInternal medicineMyelofibrosisBone marrow

Abstract

fetched live from OpenAlex

Chronic myelogenous leukemia (CML) is a hematologic malignancy with unique significance to the field of hematology and oncology, specifically due to the development of tyrosine kinase inhibitors (TKIs). CML often presents with nonspecific symptoms, and the quality of life in patients with CML has drastically improved as a result of TKIs. However, complications of CML including the risk of transforming into life-threatening blast crises continue to exist. Further, as most patients are asymptomatic in the chronic phase, patients often present with serious complications associated with noncompliance to TKIs. For example, central nervous system (CNS) manifestations of CML have been reported, both as the initial presentation of undiagnosed CML and as known complication of uncontrolled CML. Hyperleukocytosis is a manifestation of uncontrolled CML and leukostasis is a complication, occurring in cases of acute myeloid leukemia (AML). Here we present a rare case of leukostasis in a patient with known CML presenting on computed tomography (CT) as intracranial masses in the chronic phase. Our goal is to discuss this rare case of leukostasis in adult CML and describe its management.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.022
GPT teacher head0.290
Teacher spread0.268 · 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 designBench or experimental
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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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