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Record W2976049443

Treatment of older patients with acute myeloid leukemia (AML): revised Canadian consensus guidelines.

2017· review· en· W2976049443 on OpenAlexaffabout
Joseph Brandwein, Nancy Zhu, Rajat Kumar, Brian Leber, Mitchell Sabloff, Irwindeep Sandhu, Jeannine Kassis, Harold J. Olney, Mohamed Elemary, Andre C. Schuh

Bibliographic record

VenuePubMed · 2017
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsSaskatchewan Cancer AgencyUniversity of OttawaMcMaster UniversityPrincess Margaret Cancer CentreUniversity of ManitobaUniversity of SaskatchewanUniversité de MontréalUniversity of Alberta
Fundersnot available
KeywordsMedicineGemtuzumab ozogamicinInternal medicineCytarabineInduction chemotherapyAcute promyelocytic leukemiaOncologyIdarubicinMyeloid leukemiaAzacitidineHematopoietic stem cell transplantationLeukemiaDecitabineChemotherapyTransplantationStem cell
DOInot available

Abstract

fetched live from OpenAlex

is the recommended dose for 3+7 induction therapy. Acute promyelocytic leukemia should be treated with arsenic trioxide plus all-trans retinoic acid, regardless of age, with cytotoxic therapy added upfront only for those with initial white blood count > 10. HSCT may be considered for selected suitable patients up to age 70-75. Haploidentical donor transplants may be considered for older patients. For non-induction candidates, azacitidine is recommended for those with adverse risk cytogenetics, while either a hypomethylating agent (HMA) or low-dose cytarabine can be used for others. HMA may also be used for relapsed/refractory disease after chemotherapy. For patients with secondary AML, CPX-351 is recommended for fit patients age 60-75.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.105
GPT teacher head0.357
Teacher spread0.252 · 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 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

Citations16
Published2017
Admission routes2
Has abstractyes

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