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Record W4221042673 · doi:10.1080/10428194.2021.2002321

Real-world treatment patterns and clinical outcomes in patients with AML unfit for first-line intensive chemotherapy <sup>*</sup>

2022· article· en· W4221042673 on OpenAlexaff
Toshihiro Miyamoto, David Sanford, Ciprian Tomuleasa, Hui‐Hua Hsiao, Leonardo José Enciso Olivera, Anoop Enjeti, Alberto Giménez Conca, Teresa Bernal, Larisa Girshova, Maria Paola Martelli, Birol Güvenç, Alexander Delgado, Yinghui Duan, Belen Garbayo Guijarro, Cynthia Llamas, Je‐Hwan Lee

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of British Columbia
FundersAbbVie
KeywordsMedicineCytarabineInternal medicineMyeloid leukemiaRetrospective cohort studyOncologyChemotherapy

Abstract

fetched live from OpenAlex

Acute myeloid leukemia (AML) predominantly affects the elderly, and prognosis declines with age. Induction chemotherapy plus consolidation therapy is standard of care for fit patients; options for unfit patients include hypomethylating agents (HMA), low-dose cytarabine (LDAC), targeted therapies, and best supportive care (BSC). This retrospective chart review evaluated clinical outcomes in unfit patients with AML who initiated first-line treatment or BSC 01/01/2015-12/31/2018. Overall survival (OS), progression-free survival (PFS), time-to-treatment failure (TTF), and response rates were assessed. Of 1762 patients, 1310 received systemic therapies: 809 HMA, 199 LDAC, and 302 other therapies; 452 received BSC. Median OS was 9.9, 7.9, 5.4, and 2.5 months for HMA, LDAC, other, and BSC, respectively. Median PFS was 7.5, 5.3, 4.1, and 2.1 months for HMA, LDAC, other, and BSC, respectively; median TTF was 4.9, 2.1, 2.2, and 2.1 months, respectively. Our findings highlight the unmet need for novel therapies for unfit patients.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.020
GPT teacher head0.301
Teacher spread0.281 · 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.

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

Citations29
Published2022
Admission routes1
Has abstractyes

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