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Record W2795076534 · doi:10.1016/j.blre.2018.03.006

Time to repeal and replace response criteria for acute myeloid leukemia?

2018· review· en· W2795076534 on OpenAlexaff
Clara D. Bloomfield, Elihu H. Estey, Lisa Pleyer, Andre C. Schuh, Eytan M. Stein, Martin S. Tallman, Andrew H. Wei

Bibliographic record

VenueBlood Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
FundersBristol-Myers SquibbAgios PharmaceuticalsCelgeneDaiichi-SankyoPfizerAmgen
KeywordsMedicineClinical trialIntensive care medicineMinimal residual diseaseMyeloid leukemiaOncologyDiseaseImmunologyInternal medicineLeukemia

Abstract

fetched live from OpenAlex

The International Working Group (IWG) response criteria for acute myeloid leukemia, published in 2003, have remained the standard by which the efficacy of new drugs is measured in clinical trials. Over the last decade, concepts related to treatment response have been challenged by several factors; for example, the dissociation between early clinical response and survival outcome in older patients, the recognition that epigenetic and newer differentiating-agent therapies may produce delayed responses and also hematologic improvement/transfusion independence without a morphologic response, and evidence that remissions without minimal (or measurable) residual disease (MRD) may result in outcomes superior to those of morphologic remissions with persistent MRD. The evolving role of MRD status as a potential surrogate for predicting long-term survival has enhanced the clinical need to standardize and incorporate emerging technologies that enable deeper responses beyond those recognized by the IWG, and to pre-emptively identify patients at risk of early relapse. The potential for therapeutic interventions to erase MRD and alter the natural history represents an important and open research question. Reviewed here are some of the implications and challenges associated with establishing and incorporating new treatment response criteria, initially into clinical research, and eventually into real-world practice.

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)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0020.006
Open science0.0020.001
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0030.002

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 head0.072
GPT teacher head0.416
Teacher spread0.344 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations62
Published2018
Admission routes1
Has abstractyes

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