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Record W2163596899 · doi:10.1016/j.juro.2015.02.2377

MP62-03 ROBOT ASSISTED RADICAL PROSTATECTOMY IN HIGH RISK PROSTATE CANCER

2015· article· en· W2163596899 on OpenAlexaboutno aff
Benjamin Katz, Sue-Jean Yu, Andrew J. Lightfoot, Giovanni Greaves, Elton Llukani, Alice McGill, Kelly Monahan, David I. Lee

Bibliographic record

VenueThe Journal of Urology · 2015
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsProstatectomyMedicineProstate cancerRadical retropubic prostatectomyCancerGeneral surgeryUrologyGynecologyInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyProstate Cancer: Localized V1 Apr 2015MP62-03 ROBOT ASSISTED RADICAL PROSTATECTOMY IN HIGH RISK PROSTATE CANCER Benjamin Katz, Sue-Jean Yu, Andrew Lightfoot, Giovanni Greaves, Elton Llukani, Alice McGill, Kelly Monahan, and David Lee Benjamin KatzBenjamin Katz More articles by this author , Sue-Jean YuSue-Jean Yu More articles by this author , Andrew LightfootAndrew Lightfoot More articles by this author , Giovanni GreavesGiovanni Greaves More articles by this author , Elton LlukaniElton Llukani More articles by this author , Alice McGillAlice McGill More articles by this author , Kelly MonahanKelly Monahan More articles by this author , and David LeeDavid Lee More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2377AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Robot assisted radical prostatectomy (RARP) in the treatment paradigm of high-risk prostate cancer has not been well studied to date. Punnen et al reported similar results for open retropubic radical prostatectomy (RRP) as compared to RARP in men with high-risk prostate cancer. RRP utilized as monotherapy for Gleason ≥8 in 188 men showed a disease-free survival, at 5 and 7 years, of 71% and 55% respectively. Our goal was to evaluate our experience in all patients with high-risk prostate cancer undergoing RARP at our institution. METHODS We performed a single-institution, single-surgeon review of 3,433 patients who underwent RARP from 2005 to 2014. Patients were identified who underwent RARP for high-risk disease as determined by D'Amico classification (Gleason 8- 10, stage ≥T2c, and or PSA≥20). Perioperative data was evaluated from a prospectively maintained RARP database. Treatment was considered a failure with PSA levels ≥0.2. RESULTS In total, 363 patients were identified that met high-risk classification. The median age was 63. The median BMI was 29.24. Follow-up ranged from 3 to 98 months. 1-year biochemical recurrence free survival (1-RFS) was 81.2%. 2- year biochemical recurrence free survival (2-RFS) was 63.3%. Gleason score of 8-10 was the most common pre- operative high-risk identifier (66%). Positive surgical margins occurred in 32.8% of patients. Lymph node dissection was performed on 351 patients (97%). The 12 patients that did not receive a lymph node dissection had surgery. In total, 19 patients (5.4%) were found to have positive lymph node involvement. CONCLUSIONS We present our data for the treatment of high-risk prostate cancer with RARP. Consistent with recent, but limited data, RARP appears to be an effective initial treatment for high-risk prostate cancer. Further follow-up for these patients remains necessary to help better understand long-term outcomes following RARP for high-risk prostate cancer. Preoperative Patient Data High Risk Patients (n) 363 Gleason: 6 82 7 42 8 183 9 50 10 6 Clinical Stage: T1C 221 T2A 53 T2B 3 T2C 71 T3A 11 T3B 2 PSA Median (range) 6.06 (0.65-80) N>20 PSA 68 Postoperative Patient Data Gleason: 6 38 7 215 8 72 9 36 10 2 Pathologic Stage T2A 8 T2B 4 T2C 181 T3A 88 T3B 79 T4 3 % Surgical Margins 32.78% % Extracapsular Extension 40.5% Estimated Blood Loss (ml) 112 © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e780-e781 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Benjamin Katz More articles by this author Sue-Jean Yu More articles by this author Andrew Lightfoot More articles by this author Giovanni Greaves More articles by this author Elton Llukani More articles by this author Alice McGill More articles by this author Kelly Monahan More articles by this author David Lee More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.130
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1300.031

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 head0.027
GPT teacher head0.300
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreOther

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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Citations0
Published2015
Admission routes1
Has abstractyes

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