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Leukemias

2006· other· en· W4254072715 on OpenAlexaff
Patricia Disperati, Fernando Suárez, Haytham Khoury, Mark D. Minden

Bibliographic record

VenueTNM Online · 2006
Typeother
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of TorontoOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsBone marrowMedicineLeukemiaAcute leukemiaImmunologyMyeloidMyeloid leukemiaStem cellMalignant transformationBiologyCancer researchGenetics

Abstract

fetched live from OpenAlex

Abstract Leukemia, the malignant transformation of bone marrow blood‐forming cells, is not a single entity. Prior to any form of therapy, which would include antibiotics and transfusion, the leukemias were classified as acute or chronic. The acute leukemias were rapidly fatal, with individuals dying in days to weeks from the time of diagnosis. In contrast, individuals with chronic leukemias could live for many years without any therapy. With the recognition of the stem‐cell nature of blood formation and the identification of different lineages, it was possible to further subdivide the leukemias into those of the myeloid and lymphoid lineages; with the development of therapeutics, this distinction has proven to be important.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0240.002

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 head0.023
GPT teacher head0.324
Teacher spread0.301 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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