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Record W3111382721 · doi:10.21608/mjmu.2003.127234

COEXISTANCE OF ACUTE MYELOID LEUKEMIA (AML-M4) AND HODGKIN'S LYMPHOMA: IMMUNOHISTOCHEMICAL, SKY AND INTERPHASE FISH ANALYSIS.

2003· article· en· W3111382721 on OpenAlexaff
Jaudah Al‐Maghrabi, Jana Karásková, Jeremy A. Squire

Bibliographic record

VenueMansoura Medical Journal · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMyeloid leukemiaFish <Actinopterygii>InterphaseLymphomaImmunohistochemistryLeukemiaMedicineCancer researchBiologyPathologyImmunologyFisheryGenetics

Abstract

fetched live from OpenAlex

We describe a case of 61-year-old man presented with multiple constitu­tional symptoms and found to have si­multaneous occurrence of acute myeloblastic leukemia (AML-M4) and Hodgkin's lymphoma in the cervical lymph node. Peripheral blood (PB) and bone marrow (BM) smears showed typical AML-4 features. Kar-yotyping of the bone marrow revealed many cytogenetic changes including t(16;17) and -Y. SKY analysis show the following clonal pattern 42,X,-Y,der(2)t(2;11)(q37;?), der(5;17) (p10;p10), der(16)t(16;21) (q24;q11),-18, -21. To prove that the Hodgkin's lymphoma of the cervical lymph node is not a leukemic infiltrate and that it is from different clone, interphase flu­orescence in situ hybridization (IFISH) was applied on the lymph node biop­sy using X and Y centrosome probes revealed absence of loss of chro­mosome Y. These findings indicated that this patient had a coexistence of AML and Hodgkin's lymphoma. Up to our knowledge this is the first cytogenetically proved case report of a simultaneous occurrence of these two diseases. CLINICAL HISTORY: The patient was a 61-year-old man presented with multiple costitutional symptoms including weight loss, shortness of breath, night sweat and fever. On admission he was found to

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.264
Teacher spread0.260 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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