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Record W2956168677 · doi:10.1182/blood.2019000239

Combining gene mutation with gene expression analysis improves outcome prediction in acute promyelocytic leukemia

2019· article· en· W2956168677 on OpenAlexaff
Antonio R. Lucena‐Araujo, Juan Luiz Coelho‐Silva, Diego A. Pereira‐Martins, Douglas RA Silveira, Luisa C A Koury, Raul Antônio Morais Melo, Rosane Bittencourt, Kátia Bórgia Barbosa Pagnano, Ricardo Pasqüini, Elenaide C. Nunes, Evandro M. Fagundes, Ana Glória, Fábio R. Kerbauy, Maria de Lourdes Chauffaille, Israel Bendit, Vanderson Rocha, Armand Keating, Martin S. Tallman, Raul C. Ribeiro, Richard Dillon, Arnold Ganser, Bob Löwenberg, Peter J.M. Valk, Francesco Lo‐Coco, Miguel Á. Sanz, Nancy Berliner, Eduardo Magalhães Rego

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Cancer InstituteFundação de Amparo à Pesquisa do Estado de São PauloConselho Nacional de Desenvolvimento Científico e TecnológicoAmerican Society of Hematology
KeywordsAcute promyelocytic leukemiaMyeloid leukemiaAnthracyclineOncologyMedicineRetinoic acidInternal medicineMutationTretinoinDaunorubicinGeneLeukemiaMalignancyCancer researchBioinformaticsGeneticsBiologyCancer

Abstract

fetched live from OpenAlex

Abstract Luceno-Araujo et al use assays of mutations associated with myeloid malignancy to propose an integrative prognostic score for acute promyelocytic leukemia (ISAPL) in patients treated with all-trans retinoic acid and anthracycline-based therapy. They demonstrate that the ISAPL is superior for predicting outcomes and identifying patients who may benefit from alternative therapies to maximize their chance of a cure.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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 designObservational
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

Citations25
Published2019
Admission routes1
Has abstractyes

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