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Record W4367553368 · doi:10.1158/2643-3249.aml23-ia03

Abstract IA03: Inducing apoptosis in leukemia stem cells by targeting metabolism

2023· article· en· W4367553368 on OpenAlex

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.

Bibliographic record

VenueBlood Cancer Discovery · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMyeloid leukemiaLeukemiaCancer researchStem cellApoptosisMedicineCancerPopulationDiseaseMyelodysplastic syndromesCancer stem cellImmunologyBiologyBone marrowInternal medicineCell biologyGenetics

Abstract

fetched live from OpenAlex

Abstract Outcomes for patients with acute myeloid leukemia (AML) remain poor in part due to the inability of current therapeutic regimens to fully eradicate disease initiating leukemia stem cells (LSCs). We and others have shown that LSCs have unique and targetable metabolic vulnerabilities that can be exploited to induce apoptosis and kill this essential cell population. Interestingly the metabolic vulnerabilities that exist in LSCs appear to be highly dependent on the stage of disease pathogenesis, mutation status, and cellular state, suggesting that accounting for multiple biological factors is required when targeting metabolism in AML. Citation Format: Courtney Jones. Inducing apoptosis in leukemia stem cells by targeting metabolism [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr IA03.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.288
Teacher spread0.267 · 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