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Record W2903296906 · doi:10.1097/hs9.0000000000000159

Maintenance Therapy With Interleukin‐2 for Childhood AML

2018· article· en· W2903296906 on OpenAlexfundno aff
Arnaud Petit, Stéphane Ducassou, Thierry Leblanc, Marlène Pasquet, Alexandra Rousseau, Christine Ragu, Marine Cachanado, Brigitte Nelken, Yves Bertrand, Gérard Michel, Virginie Gandemer, Wendy Cuccuini, Odile Fenneteau, Hélène Lapillonne, Anne Auvrignon, André Baruchel, Guy Leverger

Bibliographic record

VenueHemaSphere · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
FundersAssistance publique-Hôpitaux de ParisInstitut National Du CancerFondation ARC pour la Recherche sur le CancerCNIBSociété Française de lutte contre les Cancers et les leucémies de l'Enfant et de l'AdolescentAssociation Laurette Fugain
KeywordsMedicineCytarabineInternal medicineMitoxantroneMaintenance therapyAcute myeloblastic leukemiaPopulationRandomized controlled trialLeukemiaChemotherapySurgeryOncology

Abstract

fetched live from OpenAlex

= 0.07). No deaths from toxicity or excess of serious adverse events related to IL2 treatment were recorded. Prolonged IL2 for maintenance therapy after intensive chemotherapy is feasible and safe in pediatric AML patients in their first CR. Such treatment did not improve DFS in this study, but a positive trend was observed in favor of IL2 maintenance therapy among core binding factor acute myeloblastic leukemia.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

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)0.0010.000

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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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
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

Citations25
Published2018
Admission routes1
Has abstractyes

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