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Record W1988964337 · doi:10.4021//jmc.v3i1.404

Secondary Hairy Cell Leukemia in a Patient With Chronic Myelogenous Leukemia Following Treatment With Tyrosine Kinase Inhibitors: Report of an Extremely Rare Case and Review of the Literature

2012· article· en· W1988964337 on OpenAlexvenueno aff
Reza Setoodeh, Deniz Peker, Andrew L. Feldman, Mark E. Law, Ling Zhang, Lynn C. Moscinski, Haipeng Shao

Bibliographic record

VenueJournal of Medical Cases · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChronic myelogenous leukemiaHairy cell leukemiaTyrosine kinaseLeukemiaCancer researchCD135Tyrosine-kinase inhibitorInternal medicineCancerReceptor

Abstract

fetched live from OpenAlex

The occurrence of chronic myelogenous leukemia (CML) and hairy cell leukemia (HCL) in the same patient is extremely rare. The reported cases had either CML and HCL occurring simultaneously or development of CML after HCL. We report an unusual case of CML with subsequent development of HCL after treatment with tyrosine kinase inhibitors. The diagnosis was challenging both clinically and pathologically due to the expected side effects of tyrosine kinase inhibitors and low levels of hairy cells initially. We further showed that the CML and HCL are not clonally related, in contrast to the only case in which clonality study was performed. To our knowledge, this is the first report of HCL developing in patients with CML. J Med Cases. 2012;3(1):39-42 doi: https://doi.org/10.4021/jmc404w

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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