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PD46-01 IMPACT OF TIME-TO-SURGERY AND SURGICAL DELAY ON ONCOLOGIC OUTCOMES FOR RENAL CELL CARCINOMA

2019· article· en· W4233430423 on OpenAlexaboutno aff
Benjamin Shiff, Rodney H. Breau, Premal Patel, Anil Kapoor, Frédéric Pouliot, Alan So, Ricardo Rendon, Ron Moore, Antonio Finelli, Simon Tanguay, Luke T. Lavallée, Jean‐Baptiste Lattouf, Jun Kawakami, Daniel Yick Chin Heng, Ranjeeta Mallick, Darrell Drachenberg

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRenal cell carcinomaOncologic surgeryCarcinomaSurgeryGeneral surgeryOncologyUrologyInternal medicine

Abstract

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You have accessJournal of UrologyKidney Cancer: Localized: Surgical Therapy V (PD46)1 Apr 2019PD46-01 IMPACT OF TIME-TO-SURGERY AND SURGICAL DELAY ON ONCOLOGIC OUTCOMES FOR RENAL CELL CARCINOMA Benjamin Shiff*, Rodney Breau, Premal Patel, Anil Kapoor, Frederic Pouliot, Alan So, Ricardo Rendon, Ron Moore, Antonio Finelli, Simon Tanguay, Luke Lavallee, Jean-Baptiste Lattouf, Jun Kawakami, Daniel Heng, Ranjeeta Mallick, and Darrell Drachenberg Benjamin Shiff*Benjamin Shiff* More articles by this author , Rodney BreauRodney Breau More articles by this author , Premal PatelPremal Patel More articles by this author , Anil KapoorAnil Kapoor More articles by this author , Frederic PouliotFrederic Pouliot More articles by this author , Alan SoAlan So More articles by this author , Ricardo RendonRicardo Rendon More articles by this author , Ron MooreRon Moore More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Simon TanguaySimon Tanguay More articles by this author , Luke LavalleeLuke Lavallee More articles by this author , Jean-Baptiste LattoufJean-Baptiste Lattouf More articles by this author , Jun KawakamiJun Kawakami More articles by this author , Daniel HengDaniel Heng More articles by this author , Ranjeeta MallickRanjeeta Mallick More articles by this author , and Darrell DrachenbergDarrell Drachenberg More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556745.68596.55AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Surgical wait times (SWT) are a major concern in healthcare, with hospitals burdened by increased demand and limited resources. In 2005, the Canadian Surgical Wait Time Consensus statement suggested wait times of <90 days for T1a, and <28 days for >/=T1b asymptomatic renal masses. Few reports have examined the effect of prolonged SWT for renal cancer surgery on oncologic outcomes, and those that exist were conducted on a single institution level. We aimed to evaluate whether SWT is associated with treatment outcomes for renal masses on a multi-institution level. METHODS: The Canadian Kidney information system (CKCis) is a national multi-institution database of patients with kidney tumors. This database was used to identify a historical cohort of patients who underwent surgery for >/= clinical stage T1b renal cell carcinoma (RCC) from 2011 onwards. Time from final imaging prior to surgery to the date of surgery was used as a measure of SWT. Oncologic outcomes such as recurrence-free survival, cancer-specific survival, and overall survival were stratified by clinical stage and SWT to assess for associations between SWT and outcomes. RESULTS: Of 1,395 patients included in the analysis, 664 (47.6%) were categorized as stage cT1b, 387 (27.7%) as stage cT2, and 344 (24.7%) as stage cT3/4. Mean follow-up duration was 28.80 months. Mean SWT was 61.6 days, 39.3 days, and 31.5 days for stage cT1b, cT2, and cT3/4, respectively. Among cT1b, cT2, and cT3/4 patients, SWT exceeded 12 weeks in 27.4%, 11.6%, and 8.1% of patients, respectively. There was no association between SWT and recurrence-free survival, margin status, or lymph node status for tumors of all clinical stages. CONCLUSIONS: Mean SWTs for renal cancer surgery appear to be within recommendations, though a significant proportion of cT1b patients are experiencing prolonged wait times. Patients who had longer SWTs in this study did not experience negative oncologic outcomes such as positive margins, positive lymph nodes, or worse recurrence free survival among all clinical stages. Source of Funding: None Winnipeg, Canada; Ottawa, Canada; Miami, FL; Hamilton, Canada; Quebec City, Canada; Vancouver, Canada; Halifax, Canada; Edmonton, Canada; Toronto, Canada; Montreal, Canada; Calgary, Canada; Montreal, Canada; Calgary, Canada; Edmonton, Canada; Ottawa, Canada; Winnipeg, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e831-e832 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Benjamin Shiff* More articles by this author Rodney Breau More articles by this author Premal Patel More articles by this author Anil Kapoor More articles by this author Frederic Pouliot More articles by this author Alan So More articles by this author Ricardo Rendon More articles by this author Ron Moore More articles by this author Antonio Finelli More articles by this author Simon Tanguay More articles by this author Luke Lavallee More articles by this author Jean-Baptiste Lattouf More articles by this author Jun Kawakami More articles by this author Daniel Heng More articles by this author Ranjeeta Mallick More articles by this author Darrell Drachenberg More articles by this author Expand All 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)0.008
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 score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0430.005

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 head0.016
GPT teacher head0.347
Teacher spread0.332 · 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
Published2019
Admission routes1
Has abstractyes

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