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Candidate surrogate endpoints in advanced prostate cancer: Aggregate meta-analysis of 143 randomized trials.

2022· article· en· W4281750499 on OpenAlexaff
Laila A. Gharzai, Ralph Jiang, E Jaworski, Krystal Morales, Robert T. Dess, Will Jackson, Holly Hartman, Rohit Mehra, Amar U. Kishan, Abhishek A. Solanki, Edward M. Schaeffer, Felix Y. Feng, Nicholas G. Zaorsky, Alejandro Berlín, Lee Ponsky, Jonathan E. Shoag, Yilun Sun, Matthew J. Schipper, Jorge A. García, Daniel E. Spratt

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersNational Institutes of HealthProstate Cancer Foundation
KeywordsMedicineProstate cancerInternal medicineClinical endpointHazard ratioOncologyRandomized controlled trialSurrogate endpointProgression-free survivalProstateUrologyCancerConfidence intervalOverall survival

Abstract

fetched live from OpenAlex

5039 Background: The Intermediate Clinical Endpoints (ICEs) in Cancer of the Prostate (ICECaP) working group identified metastasis-free survival as a valid surrogate endpoint for overall survival (OS) for patients with localized prostate cancer. No comparably validated surrogate endpoints for OS exist in advanced prostate cancer. Methods: In this meta-analysis, PubMed was searched for trials in advanced prostate cancer, defined as node positive (N1M0), metastatic castration-sensitive (mCSPC), non-metastatic (M0CRPC), or metastatic castration-resistant prostate cancer (mCRPC). Eligible randomized trials were required to report OS and ≥1 intermediate clinical endpoint (ICE). ICEs included biochemical-failure (BF), clinical failure (CF), BF-free survival (BFS), progression-free survival (PFS), radiographic PFS (radiographic +/- other study defined endpoints). Candidacy for surrogacy was assessed using the second condition of the meta-analytic approach, correlation of the treatment effect of the ICE and OS, using R2 weighted by the inverse variance of the log ICE hazard ratio and defined as an R2 > 0.70. Results: A total of 143 randomized trials (n = 75,601 patients) were included. No candidate endpoints met criteria for surrogacy; R2 BF (n = 28,922) 0.42 (95%CI 0.18-0.64), BFS (n = 25,741) 0.57 (95%CI 0.37-0.73), CF (n = 22,616) 0.31 (95%CI 0.0075-0.56), PFS (n = 52,639) 0.50 (95%CI 0.35-0.63), and radiographic PFS (n = 52,548) 0.50 (95%CI 0.35-0.63). Within preplanned subgroups by castration sensitive or resistant disease, or by treatment type, neither BFS nor PFS met criteria for surrogacy. When assessing radiographically-defined progression (exclusive or with clinical progression), PFS for the overall group and by castration status did not meet criteria for surrogacy. Sensitivity analyses demonstrated that candidacy for surrogacy of all endpoints tested did not change over time. Conclusions: Our aggregate screening method for surrogate endpoints in advanced prostate cancer demonstrated commonly used clinical endpoints are not valid surrogate endpoints for OS, and further composite endpoint construction is necessary.

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)0.042
metaresearch head score (Gemma)0.059
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: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.059
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0140.048
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.308
GPT teacher head0.556
Teacher spread0.248 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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