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Record W4362475482 · doi:10.1016/j.heliyon.2023.e14924

Expert consensus on microtransplant for acute myeloid leukemia in elderly patients -report from the international microtransplant interest group

2023· review· en· W4362475482 on OpenAlexaff
Hui‐Sheng Ai, Nelson J. Chao, David A. Rizzieri, Xiao‐Jun Huang, Thomas R. Spitzer, Jianxiang Wang, Mei Guo, Armand Keating, Elizabeth F. Krakow, Didier Blaise, Jun Ma, Depei Wu, John L. Reagan, Usama Gergis, Rafael F. Duarte, Preet M. Chaudhary, Kai‐Xun Hu, Chang-Lin Yu, Qi‐Yun Sun, Ephraim J. Fuchs, Bo Cai, Yajing Huang, Jian‐Hui Qiao, David Gottlieb, Kirk R. Schultz, Mingyao Liu, Xiequn Chen, Wen-Ming Chen, Jianmin Wang, Xiaohui Zhang, Jianyong Li, He Huang, Zimin Sun, Fei Li, Linhua Yang, Liansheng Zhang, Lijuan Li, Kai‐Yan Liu, Jie Jin, Qifa Liu, Dai‐Hong Liu, Chunji Gao, Chuanbo Fan, Wei Li, Xi Zhang, Liangding Hu, Weijing Zhang, Yuyang Tian, Weidong Han, Jun Zhu, Zhijian Xiao, Daobin Zhou, Bolong Zhang, Yong-Qian Jia, Yongqing Zhang, Xiaoxiong Wu, Xuliang Shen, Xuzhang Lu, Xin-Rong Zhan, Xiuli Sun, Yi Xiao, Jingbo Wang, Xiaodong Shi, B. Zheng, Jieping Chen, Banghe Ding, Zhao Wang, Fan Zhou, Mei Zhang, Yizhuo Zhang, Jie Sun, Bing Xia, Baoan Chen, Liangming Ma

Bibliographic record

VenueHeliyon · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsBC Children's HospitalUniversity of Toronto
FundersNational Natural Science Foundation of Chinabluebird bioPfizer
KeywordsMedicineMyeloid leukemiaInternal medicineIntensive care medicineOncologyFamily medicine

Abstract

fetched live from OpenAlex

Recent studies have shown that microtransplant (MST) could improve outcome of patients with elderly acute myeloid leukemia (EAML). To further standardize the MST therapy and improve outcomes in EAML patients, based on analysis of the literature on MST, especially MST with EAML from January 1st, 2011 to November 30th, 2022, the International Microtransplant Interest Group provides recommendations and considerations for MST in the treatment of EAML. Four major issues related to MST for treating EAML were addressed: therapeutic principle of MST (1), candidates for MST (2), induction chemotherapy regimens (3), and post-remission therapy based on MST (4). Others included donor screening, infusion of donor cells, laboratory examinations, and complications of treatment.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.083
GPT teacher head0.370
Teacher spread0.288 · 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 designNot applicable
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

Citations5
Published2023
Admission routes1
Has abstractyes

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