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Record W4291035494 · doi:10.1016/j.urolonc.2022.06.018

Sequencing impact and prognostic factors in metastatic castration-resistant prostate cancer patients treated with cabazitaxel: A systematic review and meta-analysis

2022· review· en· W4291035494 on OpenAlexaff
Takafumi Yanagisawa, Tatsushi Kawada, Paweł Rajwa, Hadi Mostafaei, Reza Sari Motlagh, Fahad Quhal, Ekaterina Laukhtina, Frederik König, Maximilian Pallauf, Benjamin Pradère, Pierre I. Karakiewicz, Péter Nyírády, Takahiro Kimura, Shin Egawa, Shahrokh F. Shariat

Bibliographic record

VenueUrologic Oncology Seminars and Original Investigations · 2022
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCabazitaxelMedicineDocetaxelProstate cancerHazard ratioMeta-analysisInternal medicineOncologyConfidence intervalSubgroup analysisCancerAndrogen deprivation therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Cabazitaxel is an effective treatment of post-docetaxel metastatic castration-resistant prostate cancer (mCRPC). We aimed to assess the sequencing impact and identify prognostic factors of oncologic outcomes in mCRPC patients treated with cabazitaxel. METHODS: PUBMED, Web of Science, and Scopus databases were searched for articles published before January 2022 according to the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) statement. Studies were deemed eligible if they investigated pretreatment clinical or hematological prognostic factors of overall survival (OS) in mCRPC patients with progression after docetaxel treated with available treatments including cabazitaxel. RESULTS: Overall, 22 studies were eligible for the meta-analysis. In mCRPC patients treated with docetaxel, subsequent treatment with cabazitaxel was associated with better OS compared to that without cabazitaxel (pooled hazard ratio [HR]: 0.70, 95% confidence interval [CI]: 0.56-0.89). Among the patients treated with cabazitaxel, several pretreatment clinical features and hematologic biomarkers were associated with worse OS as follows: poor performance status (PS) (pooled HR: 1.92, 95% CI: 1.33-2.77), presence of visceral metastasis (pooled HR: 2.13, 95% CI: 1.62-2.81), symptomatic disease (pooled HR: 1.47, 95% CI: 1.25-1.73), high PSA (pooled HR: 1.76, 95% CI: 1.27-2.44), high alkaline phosphatase (ALP) (pooled HR: 1.45, 95% CI: 1.28-1.65), high lactate dehydrogenase (LDH) (pooled HR: 1.54, 95% CI: 1.00-2.38), high c-reactive protein (CRP) (pooled HR: 4.40, 95% CI: 1.52-12.72), low albumin (pooled HR:1.09, 95% CI: 1.05-1.12) and low hemoglobin (pooled HR:1.55, 95% CI: 1.20-1.99). CONCLUSIONS: Sequential therapy with cabazitaxel significantly improves OS in post-docetaxel mCRPC patients. In mCRPC patients treated with cabazitaxel, patients with poor PS, visceral metastasis, and symptomatic disease were associated with worse OS. Further, pretreatment high PSA, ALP, LDH or CRP as well as low hemoglobin or albumin, were blood-based prognostic factors for OS. These findings might help guide the clinical decision-making for the use of cabazitaxel and prognostication of its OS benefit.

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)0.012
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.042
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.144
GPT teacher head0.413
Teacher spread0.270 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations19
Published2022
Admission routes1
Has abstractyes

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