MP74-18 IMPACT OF SURGICAL WAIT TIMES DURING SUMMER MONTHS ON THE ONCOLOGICAL OUTCOMES FOLLOWING ROBOTIC-ASSISTED RADICAL PROSTATECTOMY: 10 YEARS’ EXPERIENCE FROM A LARGE CANADIAN ACADEMIC CENTER
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Localized: Surgical Therapy IV (MP74)1 Apr 2020MP74-18 IMPACT OF SURGICAL WAIT TIMES DURING SUMMER MONTHS ON THE ONCOLOGICAL OUTCOMES FOLLOWING ROBOTIC-ASSISTED RADICAL PROSTATECTOMY: 10 YEARS’ EXPERIENCE FROM A LARGE CANADIAN ACADEMIC CENTER Ahmed Sayed Zakaria*, Félix Couture, David-Dan Nguyen, Hanna Shahine, Côme Tholomier, Cristina Negrean, Kyle Law, Pierre Karakiewicz Karakiewicz, Assaad El-Hakim, and Kevin Zorn Ahmed Sayed Zakaria*Ahmed Sayed Zakaria* More articles by this author , Félix CoutureFélix Couture More articles by this author , David-Dan NguyenDavid-Dan Nguyen More articles by this author , Hanna ShahineHanna Shahine More articles by this author , Côme TholomierCôme Tholomier More articles by this author , Cristina NegreanCristina Negrean More articles by this author , Kyle LawKyle Law More articles by this author , Pierre Karakiewicz KarakiewiczPierre Karakiewicz Karakiewicz More articles by this author , Assaad El-HakimAssaad El-Hakim More articles by this author , and Kevin ZornKevin Zorn More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000960.018AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Most Canadian hospitals face significant reductions (20-50%) in operative room access during summer months due to nursing shortages, leading to increased surgical delays. Hence, we sought to assess the impact of this extra-wait time to undergo robotic-assisted radical prostatectomy (RARP) on the post-operative oncological outcomes METHODS: We conducted a retrospective review of a prospectively maintained RARP database in two high-volume academic centers, between 2010 and 2019. Wait time was defined as the interval between surgical booking and RARP. Assessed outcomes included impact on the difference between post-biopsy USCF-CAPRA and post-surgical CAPRA-S scores, biochemical recurrence (BCR) rates and Gleason score upgrade on surgical specimen. Multivariable analysis (MVA) with regression models was used to evaluate the effect of wait times RESULTS: A total of 1057 men were included for analysis. Consistent over a 10-year period, analysis of mean surgical/operative booking volumes (Fig.1-A), revealed that summer months had the lowest surgical volumes output despite above average booking volumes. The lowest surgical volume occurred during July (7.1case/month), which was 35% less than the cohort average. Moreover, summer months had the longest average time between surgical booking date and RARP, with the longest wait occurring for patients booked in June (average 93±69 days, p<0.001). On MVA, patients booked in June had significantly more chance of having an increase in CAPRA score [HR (95%CI) 1.64 (1.02-2.63); p=0.04] and in CAPRA risk group [HR (95%CI) 1.82 (1.04-3.19)] after surgery compared to patients booked in other months. Cohort analysis (Fig.1-B) showed fair correlation between CAPRA-score difference and time between booking and RARP (r=-0.062; p=0.044) CONCLUSIONS: Our cohort results demonstrate that conventional RARP wait times are significantly prolonged during summer months, with worse post-RARP oncological outcomes in terms of CAPRA score, which is associated with a higher risk of BCR. Further national studies are required to address these delays in other oncological populations. Moreover, other compensatory mechanisms to sustain consistent yearly operative output should be considered Source of Funding: None © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1138-e1139 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ahmed Sayed Zakaria* More articles by this author Félix Couture More articles by this author David-Dan Nguyen More articles by this author Hanna Shahine More articles by this author Côme Tholomier More articles by this author Cristina Negrean More articles by this author Kyle Law More articles by this author Pierre Karakiewicz Karakiewicz More articles by this author Assaad El-Hakim More articles by this author Kevin Zorn More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".