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Record W3158519315 · doi:10.15173/sciential.v1i4.2425

Battling Acute Myeloid Leukemia (AML) as both a Clinician and Scientist

2020· article· en· W3158519315 on OpenAlex
Reza Khorvash, Ram Upadhyaya

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueSciential - McMaster Undergraduate Science Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEpigeneticsMyeloid leukemiaCancer researchStem cellHaematopoiesisBone marrowDNA methylationLeukemiaChemotherapyMyeloidChromatinMedicineHematopoietic stem cell transplantationBiologyGeneAcute leukemiaImmunologyInternal medicineGene expressionGenetics

Abstract

fetched live from OpenAlex

Acute myeloid leukemia (AML) is characterized by the accumulation of immature hematopoietic cells in the bone marrow that impair normal blood formation. Chemotherapy is always the first treatment option for AML. Patients can also be cured by allogeneic stem cell transplantation, which consists of transferring stem cells from a healthy donor to the patient after high-intensity (high-dose) chemotherapy. Many mutations found in AML affect the cells on an epigenetic level, influencing the gene expression of the cells such as influencing DNA-methylation genes and chromatin-modifying genes. Affecting these epigenetic mechanisms is therefore of great interest in the area of AML.

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0030.004
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.325
Teacher spread0.295 · 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