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Record W2334242996 · doi:10.1016/j.juro.2014.02.488

PD10-12 CLINICAL OUTCOMES FOLLOWING LAPAROSCOPIC MANAGEMENT OF PT3 RENAL MASSES: A MULTI-INSTITUTIONAL ANALYSIS

2014· article· en· W2334242996 on OpenAlexaboutno aff
Jasmir G. Nayak, Premal A. Patel, Kamaljot S. Kaler, Zhihui Liu, Anil Kapoor, Ricardo Rendon, Simon Tanguay, Peter C. Black, Jun Kawakami, Rodney H. Breau, Antonio Finelli, Laurence Klotz, Darrel Drachenberg

Bibliographic record

VenueThe Journal of Urology · 2014
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNephrectomyRenal cell carcinomaKidney cancerGeneral surgeryUrologyKidneyOncologyInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyKidney Cancer: Evaluation/Staging I1 Apr 2014PD10-12 CLINICAL OUTCOMES FOLLOWING LAPAROSCOPIC MANAGEMENT OF PT3 RENAL MASSES: A MULTI-INSTITUTIONAL ANALYSIS Jasmir Nayak, Premal Patel, Kamaljot Kaler, Zhihui Liu, Anil Kapoor, Ricardo Rendon, Simon Tanguay, Peter Black, Jun Kawakami, Rodney Breau, Antonio Finelli, Laurence Klotz, and Darrel Drachenberg Jasmir NayakJasmir Nayak More articles by this author , Premal PatelPremal Patel More articles by this author , Kamaljot KalerKamaljot Kaler More articles by this author , Zhihui LiuZhihui Liu More articles by this author , Anil KapoorAnil Kapoor More articles by this author , Ricardo RendonRicardo Rendon More articles by this author , Simon TanguaySimon Tanguay More articles by this author , Peter BlackPeter Black More articles by this author , Jun KawakamiJun Kawakami More articles by this author , Rodney BreauRodney Breau More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Laurence KlotzLaurence Klotz More articles by this author , and Darrel DrachenbergDarrel Drachenberg More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.488AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Technological advances and improved experience have resulted in advanced renal masses being treated by minimally invasive techniques. We report on the oncological outcomes of patients with non-metastatic renal masses with vascular invasion (pT3) who were treated with laparoscopic nephrectomy (LN) and/or caval thrombectomy. METHODS Institutional databases on patients treated surgically for renal cell carcinoma (RCC) were obtained from 14 centres across 6 Canadian provinces forming the Canadian Kidney Cancer Information System (CKCis) database. Data were collected on 2204 patients and included patient characteristics, peri-operative information, as well as pathological and oncological outcomes. RESULTS Of the 2204 patients, 498 (22.6%) patients had pT3 disease according to the 2009 TNM staging system. 173 (35%) patients underwent laparoscopic management, of which 135 (27%) did not have evidence of metastatic disease at the time of surgery. Mean age was 65 (range 35-88) with a higher propensity of male patients (n=87, 64%). Median tumor size was 6.5 cm (range 1 - 15 cm). The pre-operative clinical stage ranged from cT1-cT4. Average blood loss was 266 ml (range 0-4000 ml) with a mean operative time of 146 minutes (range 73-360 minutes). The majority of lesions were clear cell RCC (68%). Of the pT3 lesions, there were no peri-operative deaths (<30 days). After a median follow-up of 1.4 years, 31 (23%) patients developed metastatic disease, the vast majority being of pulmonary origin (97%). At the end of our follow-up period 130 (96%) patients were alive. CONCLUSIONS For properly selected patients, laparoscopic management of locally advanced renal masses yields acceptable oncological outcomes. Although encouraging, longer follow-up is required to further delineate its role. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e286 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Jasmir Nayak More articles by this author Premal Patel More articles by this author Kamaljot Kaler More articles by this author Zhihui Liu More articles by this author Anil Kapoor More articles by this author Ricardo Rendon More articles by this author Simon Tanguay More articles by this author Peter Black More articles by this author Jun Kawakami More articles by this author Rodney Breau More articles by this author Antonio Finelli More articles by this author Laurence Klotz More articles by this author Darrel Drachenberg 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)0.002
metaresearch head score (Gemma)0.007
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.352
Teacher spread0.301 · 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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Citations0
Published2014
Admission routes1
Has abstractyes

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