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Validating the Cancer and Aging Research Group (CARG) toxicity prediction tool in older men receiving chemotherapy for metastatic castration-resistant prostate cancer (mCRPC) and extending it to androgen receptor targeted agents.

2019· article· en· W2972616208 on OpenAlexaffabout
Shabbir M.H. Alibhai, Henriette Breunis, Richard Gregg, Aaron R. Hansen, Padraig Warde, Narhari Timilshina, George Tomlinson, Anthony M. Joshua, Neil Fleshner, Urban Emmenegger

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoPrincess Margaret Cancer CentreHealth Sciences CentreUniversity Health Network
Fundersnot available
KeywordsMedicineEnzalutamideProstate cancerCommon Terminology Criteria for Adverse EventsCabazitaxelAndrogen deprivation therapyInternal medicineDocetaxelChemotherapyCancerAdverse effectOncologyToxicityGynecologyAndrogen receptor

Abstract

fetched live from OpenAlex

11510 Background: Multiple treatment options are available for mCRPC. Being able to predict toxicity risk for different treatments in older adults can aid treatment decision-making and supportive care. The CARG tool is a promising toxicity risk prediction tool for chemotherapy, but has not been specifically validated in the mCRPC setting for either chemotherapy or androgen receptor targeted agents. We prospectively evaluated the ability of the CARG tool to predict risk of clinically relevant grade 2 and grade 3+ toxicity of treatment with chemotherapy (CHEMO) and abiraterone or enzalutamide (A/E) in older adults with mCRPC. Methods: Men age 65+ were enrolled in this prospective observational study at 3 academic centres, Princess Margaret Cancer Centre, Sunnybrook Health Sciences Centre, and Kingston Health Sciences Centre in Ontario, Canada. All grade 2 and grade 3+ toxicities were documented during cycle 1 of CHEMO or in the first 3 months of A/E via structured interviews and chart review. Lab abnormalities were documented only if resulting in emergency room visits, requiring treatment, or affecting subsequent oncologic treatment. Toxicity was rated using the Common Terminology Criteria for Adverse Events version 4. Logistic regression was performed to identify predictors of toxicity. Results: 64 men starting CHEMO (primarily docetaxel 60-75 mg/m^2, mean age 73y) and 59 men starting A/E (mean age 76y) were included. Clinically important grade 2 toxicities occurred in 86% and 70% of CHEMO and A/E patients, respectively. Grade 3+ toxicities occurred in 48% and 25% of CHEMO and A/E patients, respectively. The CARG tool was predictive of grade 3+ toxicities with CHEMO, which occurred in 22%, 53%, and 71% of low, moderate, and high risk groups (p = 0.017). However, the CARG tool was not predictive of grade 2 toxicities with CHEMO, or grade 2 or 3+ toxicities with A/E (Table). Conclusions: We provide external validation of the CARG tool in predicting grade 3+ toxicity in older men with mCRPC undergoing CHEMO. Grade 2 toxicities are very common with both CHEMO and A/E, and grade 3+ toxicity occurs in 1 in 4 older men on A/E. Additional efforts to identify men at higher risk of toxicity from various mCRPC treatments are warranted. [Table: see text]

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.179
GPT teacher head0.517
Teacher spread0.339 · 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 designObservational
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

Citations2
Published2019
Admission routes2
Has abstractyes

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