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Treatment Patterns and Healthcare Resource Utilization in Patients with FLT3-Mut and FLT3-Wt Acute Myeloid Leukemia: A Multi-Country Medical Chart Study

2017· article· en· W3081404079 on OpenAlexaboutno aff
James D. Griffin, Hongbo Yang, Yan Song, David Kinrich, Cat N. Bui

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMyeloid leukemiaInternal medicineHealth careOncologyFamily medicinePediatrics

Abstract

fetched live from OpenAlex

Introduction: FLT3 is a frequently mutated gene in acute myeloid leukemia (AML) with two main types of mutations: internal tandem duplication (ITD) and point mutations in the tyrosine kinase domain (TKD). FLT-mut AML is associated with poor prognosis. With the development of new therapies in AML, especially those targeting FLT3-mut, there is a need to understand the current treatment (tx) patterns and healthcare resource utilization (HRU) among AML patients. The current study used real-world data from medical records to evaluate tx patterns and HRU among adult AML patients. Methods: Hematologists and oncologists were recruited from 10 countries (US, Canada, UK, France, Germany, Italy, Spain, Netherlands, Japan, and South Korea) from an established physician panel. Eligible patients were randomly selected by physicians and were categorized into 6 cohorts: 1) newly diagnosed (ND) FLT3-mut patients Results: The study included 1,027 AML patients-183 FLT3-mut and 186 FLT3-wt ND patients Conclusions: Using real-world data of AML patients in multiple countries, this study reveals a considerable amount of heterogeneity of tx pattern, including many tx not consistent with tx guidelines. FLT3-mut patients tended to receive more aggressive tx, consistent with fact that the mutation confers a poor prognosis. It also demonstrates extensive HRU among these patients, particularly among R/R cohorts. The study provides timely evidence to understand the current tx landscape and to highlight the substantial unmet needs among AML patients. Disclosures Griffin: Novartis: Consultancy, Research Funding; Astellas Pharma Global Development: Consultancy. Yang: Analysis Group Inc.: Employment; Novartis Pharmaceutical Corporation: Other: Author is an employee of Analysis Group, which received consulting fees from Novartis for this study. Song: Analysis Group: Employment. Kinrich: Analysis Group: Employment. Bui: Astellas Pharma Global Development: Employment.

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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.029
GPT teacher head0.321
Teacher spread0.292 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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