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Record W2018971402 · doi:10.1002/pbc.22414

Toxicity assessment of molecularly targeted drugs incorporated into multiagent chemotherapy regimens for pediatric acute lymphocytic leukemia (ALL): Review from an international consensus conference

2010· article· en· W2018971402 on OpenAlexaff
Terzah M. Horton, Richard Sposto, Patrick A. Brown, C. Patrick Reynolds, Stephen P. Hunger, Naomi Winick, Elizabeth A. Raetz, William L. Carroll, R.J. Arceci, Michael J. Borowitz, Paul S. Gaynon, Lia Gore, Sima Jeha, Barry J. Maurer, Stuart E. Siegel, Andrea Biondi, Pamela Kearns, Aru Narendran, Lewis B. Silverman, Malcolm A. Smith, C. Michel Zwaan, James A. Whitlock

Bibliographic record

VenuePediatric Blood & Cancer · 2010
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsAlberta Cancer Foundation
FundersNational Cancer InstituteVanderbilt-Ingram Cancer CenterLadies Leukemia League
KeywordsMedicineClinical trialDosingOncologyChemotherapyBlood cancerRegimenLimitingIntensive care medicineInternal medicineCancer

Abstract

fetched live from OpenAlex

One of the challenges of incorporating molecularly targeted drugs into multi-agent chemotherapy (backbone) regimens is defining dose-limiting toxicities (DLTs) of the targeted agent against the background of toxicities of the backbone regimen. An international panel of 22 pediatric acute lymphocytic leukemia (ALL) experts addressed this issue (www.ALLNA.org). Two major questions surrounding DLT assessment were explored: (1) how toxicities can be best defined, assessed, and attributed; and (2) how effective dosing of new agents incorporated into multi-agent ALL clinical trials can be safely established in the face of disease- and therapy-related systemic toxicities. The consensus DLT definition incorporates tolerance of resolving Grade 3 and some resolving Grade 4 toxicities with stringent safety monitoring. This functional DLT definition is being tested in two Children's Oncology Group (COG) ALL clinical trials.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.353
Teacher spread0.335 · 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 designBench or experimental
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

Citations26
Published2010
Admission routes1
Has abstractyes

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