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Effect of decipher test on adjuvant treatment decision-making among men with high-risk pathology at radical prostatectomy: Results from a multicenter prospective PRO-IMPACT study.

2017· article· en· W2602547068 on OpenAlexaff
John L. Gore, Marguerite du Plessis, María Santiago‐Jiménez, Kasra Yousefi, Darby J. S. Thompson, David Chen, William R. Clark, Michael Franks, Lawrence I. Karsh, Adam S. Kibel, Hyung Lae Kim, Brian R. Lane, Yair Lotan, William T. Lowrance, Paul Maroni, Scott D. Perrapato, Edouard J. Trabulsi, Robert Waterhouse, Elai Davicioni, Daniel W. Lin

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsGenome British Columbia
Fundersnot available
KeywordsDECIPHERMedicineProstatectomyAnxietyProspective cohort studyInternal medicineOncologyProstate cancerCancerBioinformaticsPsychiatry

Abstract

fetched live from OpenAlex

24 Background: The decision to provide adjuvant therapy to men with high risk pathology after radical prostatectomy (RP) is confounded by tremendous uncertainty. We prospectively evaluated the impact of the Decipher test, which predicts metastases after RP, on men and providers decision quality. Methods: 150 adjuvant pts were enrolled by 43 urologists from 19 practices. Pts with pathologic T3 stage or positive surgical margins (SM+) after RP were included. Participating physicians provided a treatment (Tx) recommendation before and after exposure to Decipher test results. Pts completed validated surveys on health-related quality of life, decisional conflict, and PCa-related anxiety. Results: Median patient age at RP was 64 years; 67% and 50% had pT3 and SM+ pathology, respectively. Decipher classified 46%, 22% and 32% of men as low-, intermediate- and high-risk, respectively. Pre-Decipher, observation was recommended for 89%. Post-Decipher, 18% (95% CI 12-25%) of Tx recommendations changed. Men’s Decisional Conflict Scale (DCS) scores decreased (indicating higher decision quality) after exposure to Decipher results (median DCS pre-Decipher 25 [IQR 8-44], median DCS post-Decipher 19 [IQR 2-30], p<0.001), with greatest decreases in the subdomains of decision uncertainty and decision support. Low-risk Decipher results experienced a trend toward decreased PCa-specific anxiety (p=0.13) and a significant reduction in fear of PCa recurrence (p=0.02). Physicians’ median DCS scores decreased from 32 [IQR 28-36] to 28 [IQR 12-42] (p<0.001). Decipher results were associated with the decision to pursue ART in an MVA analysis (OR 1.48; 95% CI 1.19-1.85, p<0.001). Conclusions: Observation is the predominantly prescribed management strategy for men with high risk features at RP. Knowledge of Decipher results was associated with Tx decision-making: men at low risk for metastasis had higher rates of observation recommendations and men at high risk had higher rates of ART recommendations. Decision quality was improved and PCa-specific anxiety was decreased for men exposed to Decipher results. 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)0.004
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.156
GPT teacher head0.546
Teacher spread0.390 · 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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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