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Record W4401993452 · doi:10.3390/curroncol31090375

Outcomes of First Subsequent Taxane Therapy in Patients with Metastatic Castration-Resistant Prostate Cancer Who Previously Received Docetaxel Intensification for Metastatic Castration-Sensitive Prostate Cancer

2024· article· en· W4401993452 on OpenAlexaffvenue
Gabrielle Robin, Naveen S. Basappa, Scott North, Sunita Ghosh, Michael Kolinsky

Bibliographic record

VenueCurrent Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of Alberta
FundersAmerican Society of Clinical Oncology
KeywordsDocetaxelMedicineTaxaneProstate cancerCastrationOncologyCabazitaxelInternal medicineCancerProstateAndrogen deprivation therapyBreast cancerHormone

Abstract

fetched live from OpenAlex

Background: The management of advanced prostate cancer continues to evolve rapidly, particularly with the earlier use of survival-prolonging therapies in metastatic castration-sensitive prostate cancer (mCSPC). Though approved prior to the use of intensification therapy in mCSPC, taxane-based chemotherapies remain a relevant option for patients with metastatic castration-resistant prostate cancer (mCRPC). However, there is little evidence determining the outcomes of taxane chemotherapies as the first subsequent taxane (FST) in mCRPC pts who received docetaxel intensification (DI) in mCSPC. The purpose of this study is to compare outcomes between the survival-prolonging taxanes, docetaxel and cabazitaxel as FST after DI. Methods: New patient consults seen at the Cross Cancer Institute from 1 July 2014 to 31 December 2020 were retrospectively reviewed. Pts were considered eligible if they received DI for mCSPC and then received either docetaxel or cabazitaxel in mCRPC. Variables of interest were collected from electronic medical records. The primary endpoint was ≥50% PSA response at 12 weeks relative to baseline for FST. Secondary endpoints included OS from mCSPC diagnosis, as well as PFS and OS from the FST start date. PSA responses were compared using the chi-squared test, and time-based endpoints were compared using the Kaplan–Meier method. Results: In total, 34 pts were identified: docetaxel = 22 and cabazitaxel = 12 as FST. 91.2% of pts (docetaxel 95.5% vs. cabazitaxel 83.3%) received FST in 2nd line mCRPC. The median age at diagnosis (63.1 vs. 67.1 yrs, p = 0.236) and the median time to CRPC (18.6 vs. 14.2 mos, p = 0.079) were similar for docetaxel and cabazitaxel, respectively. The median time to FST (24.1 vs. 34.6 mos, p = 0.036) and OS from mCSPC diagnosis (30.9 vs. 52.7 mos, p = 0.002) were significantly shorter for pts receiving cabazitaxel vs. docetaxel. PSA responses occurred in 40.9% of pts treated with docetaxel compared to 25.0% treated with cabazitaxel (p = 0.645). There was no significant difference in median PFS (2.7 vs. 3.5 mos, p = 0.727) or median OS (11.4 vs. 8.1 mos, p = 0.132) from the time of FST for pts treated with docetaxel vs. cabazitaxel, respectively. Conclusions: Both docetaxel and cabazitaxel demonstrated activity as FST after DI in mCSPC. Pts who received cabazitaxel had a shorter time to FST and OS from mCSPC. The reasons for this may reflect clinician preference for cabazitaxel in pts with aggressive or rapidly progressing disease. No difference was found in PSA response, PFS, or OS from FST with docetaxel compared to cabazitaxel. While limited by its retrospective nature and small sample size, this study suggests that docetaxel is active as FST despite treatment with DI in mCSPC.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.425
Teacher spread0.304 · 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
Published2024
Admission routes2
Has abstractyes

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