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Record W2039100515 · doi:10.3747/co.21.2183

A Canadian Consensus on the Management of Newly Diagnosed and Relapsed Acute Promyelocytic Leukemia in Adults

2014· article· en· W2039100515 on OpenAlexaffvenueabout
Matthew D. Seftel, Michael J. Barnett, Stephen Couban, Brian Leber, John M. Storring, Wissam Assaily, B. Fuerth, Anna Christofides, Andre C. Schuh

Bibliographic record

VenueCurrent Oncology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsMcGill University Health CentreMcMaster UniversityDalhousie UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineAcute promyelocytic leukemiaArsenic trioxideCytarabineIntensive care medicineHematopoietic stem cell transplantationTransplantationOncologyChemotherapyInternal medicineRetinoic acid

Abstract

fetched live from OpenAlex

The use of all-trans-retinoic acid (atra) and anthracyclines (with or without cytarabine) in the treatment of acute promyelocytic leukemia (apl) has dramatically changed the management and outcome of the disease over the past few decades. The addition of arsenic trioxide (ato) in the relapsed setting-and, more recently, in reduced-chemotherapy or chemotherapy-free approaches in the first-line setting-continues to improve treatment outcomes by reducing some of the toxicities associated with anthracycline-based approaches. Despite those successes, a high rate of early death from complications of coagulopathy remains the primary cause of treatment failure before treatment begins. In addition to that pressing issue, clarity is needed about the use of ato in the first-line setting and the role of hematopoietic stem-cell transplantation (hsct) in the relapsed setting. The aim for the present consensus was to provide guidance to health care professionals about strategies to reduce the early death rate, information on the indications for hsct and on the use of ato in induction and consolidation in low-to-intermediate-risk and high-risk apl 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2014
Admission routes3
Has abstractyes

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