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Impact of decipher test on adjuvant and salvage treatments received following radical prostatectomy.

2018· article· en· W2793819981 on OpenAlexaff
John L. Gore, Marguerite du Plessis, Darlene Dai, Kasra Yousefi, Darby J. S. Thompson, Lawrence I. Karsh, Brian R. Lane, Michael Franks, David Chen, Mark Bandyk, Adam S. Kibel, Hyung Lae Kim, William T. Lowrance, Paul Maroni, Scott D. Perrapato, Edouard J. Trabulsi, Robert Waterhouse, Elai Davicioni, Yair Lotan, Daniel W. Lin

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsGenome British Columbia
Fundersnot available
KeywordsDECIPHERProstatectomyMedicineAdjuvantAdjuvant therapySalvage therapyProstate cancerAnxietyInternal medicineOncologyCancerBioinformaticsPsychiatryChemotherapyBiology

Abstract

fetched live from OpenAlex

112 Background: Prostate cancer (PC) patients and providers have tremendous uncertainty as they decide on intervention with adjuvant or salvage radiation therapy (ART, SRT) after radical prostatectomy (RP). We prospectively evaluated the impact of Decipher test, a genomic classifier which predicts metastasis post-RP, on providers’ decision-making for ART and SRT. Methods: 150 men considering ART and 115 men considering SRT from 19 sites across the US were enrolled. Participating providers submitted a management recommendation prior to processing the Decipher test and again after receiving test results. We then followed patients for 12 months to assess actual treatment received and patient reported decisional conflict scale (DCS) and a validated survey on PC-related anxiety. Results: Pre-Decipher, observation was recommended for 89% of adjuvant men and 58% of salvage men. Post-Decipher, 17% of treatment recommendations changed in the adjuvant arm and 30% of recommendations changed in the salvage arm. Among adjuvant men, 78% maintained their recommended management 12 months after Decipher; 76% of salvage men maintained their recommended treatment after Decipher. Among 21 adjuvant men who intensified their treatment (observation to ART or ART to ART plus androgen deprivation therapy), 5 (24%) experienced biochemical recurrence with detectable PSA. In adjuvant men, PC-specific anxiety decreased differently among Decipher risk categories (p-value = 0.045), most notably among Decipher high risk men (9.07 [7.87, 10.26] pre-Decipher, 5.61 [5.35,5.88] 12 months post-Decipher). In salvage men, PC-specific anxiety decreased differently among those whose treatment were concordant (10.28 [8.1,12.47] pre-Decipher, 7.18 [6.82,7.54] 12 months post-Decipher) and those whose treatment were intensified (p-value = 0.01), and decreased differently among low-risk and high-risk Decipher patients (p = 0.04). Conclusions: Use of the Decipher test changed treatment decisions that was consistent with the eventual treatment received in three-fourths of adjuvant and salvage men after RP. Several men that pursued ART experienced PSA progression. PC-specific anxiety decreased in both adjuvant and salvage men. Clinical trial information: NCT02080689.

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)0.001
metaresearch head score (Gemma)0.009
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.135
GPT teacher head0.539
Teacher spread0.404 · 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
Published2018
Admission routes1
Has abstractyes

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