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Record W1975987260 · doi:10.1158/1538-7445.am2011-1690

Abstract 1690: Definition of dose-limiting toxicity in phase I cancer clinical trials of molecularly targeted agents

2011· article· en· W1975987260 on OpenAlexaff
Christophe Le Tourneau, Albiruni R. Abdul Razak, Hui Gan, Simona Pop, Xavier Paolettí

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsToxicityMedicineClinical trialInternal medicineOncologyLimitingCancerGrading (engineering)Phases of clinical research

Abstract

fetched live from OpenAlex

Abstract Introduction: There is no consensus about what constitutes a dose-limiting toxicity (DLT) in phase I cancer clinical trials. We aimed to evaluate how DLTs are defined in phase I trials of molecularly targeted agents (MTA). Methods: We retrieved all phase I trials testing monotherapy with an MTA published in English after January 1st, 2000. In each trial, all items used to define DLTs were recorded. Results: Reports of 155 phase I trials evaluating 111 different MTAs were reviewed. The median DLT assessment period was 28 days [range: 7-56]. Among the 155 trials, the most common category of hematologic toxicity was “hematologic toxicity, not otherwise specified (NOS)”. Similarly, the most common category of non-hematologic toxicity was “non-hematologic toxicity, NOS”. A total of 8 other categories of hematological items and 46 categories of non-hematologic organ-specific items were employed to define DLTs in the 155 trials reviewed. Organ-specific items were reported in 111 trials (72%). Mean number of organ-specific items per trial increased from 2.03 in the 2000-2005 timeframe to 2.79 in the 2006-2010 timeframe (p=0.02). The most frequent determinant of whether a toxicity was regarded as a DLT was severity, usually assessed using the NCI CTCAE grading system. However, for any given toxicity, there was substantial variability in the degree of severity required for a toxicity to be considered a DLT. A number of other factors were increasingly found to influence the determination of DLT. Specifications about minimum duration and degree of reversibility were incorporated into the definition of a non-hematologic DLT in 13% and 12% of trials respectively. The need to delay treatment and to reduce dose-intensity because of toxicity was incorporated in the definition of DLT in 19% and 8% of trials respectively. The definition of DLT varied with administration schedule, with (near-)continuous dosing regimen often defining DLTs at lower severities. Definition of DLT did not differ according to classes of agents, to first-in-human versus not first-in-human studies or to dose escalation method used. Twenty three percent of the organ-specific DLTs defined in the trials in which non-hematologic DLTs occurred were actually encountered, suggesting an added value of these organ-specific DLTs to the generic definition. In 28 of the 155 trials (18%), toxicities were eventually considered to be DLTs even though they were not initially defined as such. Conversely, in 13 of the 155 trials (8%), toxicities were not reported as DLTs even though they were initially defined as such. Conclusions: The definition of DLT is heterogeneous across phase I cancer clinical trials of MTA administered as monotherapy and depended on the drug administration schedule. While our results do no support the standardization of the definition of DLT, we provide recommendations aiming at reducing the heterogeneity observed in the definition of DLT. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1690. doi:10.1158/1538-7445.AM2011-1690

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)0.081
metaresearch head score (Gemma)0.140
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.919
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0810.140
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0190.018
Science and technology studies0.0010.003
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.950
GPT teacher head0.737
Teacher spread0.214 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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