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MP32-05 ESTIMATING THE REAL-WORLD INCIDENCE OF URETHRAL COMPLICATIONS AFTER PROSTATE CANCER TREATMENT: A POPULATION-BASED ANALYSIS

2024· article· en· W4394801414 on OpenAlexaboutno aff
Carlos Ignacio Calvo, Keith Rourke

Bibliographic record

VenueThe Journal of Urology · 2024
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsProstate cancerIncidence (geometry)MedicinePopulationOncologyUrologyGynecologyInternal medicineCancerMathematicsEnvironmental health

Abstract

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You have accessJournal of UrologyReconstruction: Urethral Reconstruction (Including Stricture) II (MP32)1 May 2024MP32-05 ESTIMATING THE REAL-WORLD INCIDENCE OF URETHRAL COMPLICATIONS AFTER PROSTATE CANCER TREATMENT: A POPULATION-BASED ANALYSIS Carlos I. Calvo and Keith F. Rourke Carlos I. CalvoCarlos I. Calvo and Keith F. RourkeKeith F. Rourke View All Author Informationhttps://doi.org/10.1097/01.JU.0001008816.80828.35.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: There is a paucity of literature regarding the incidence of long-term urethral complications after prostate cancer treatment. Our objective is to assess the long-term incidence of urethral complications after different treatment modalities for prostate cancer (PC). METHODS: All men with a diagnosis of PC from 2002-2021 from the Alberta Cancer Registry were included. The Discharge Abstract Data & National Ambulatory Care Reporting System were searched for different treatment modalities and urologic diagnosis/procedures. Patients were allocated into 6 different groups, radical prostatectomy (RP), radiotherapy (RT), cryotherapy (Cryo), medical treatment/observation (MT/O), RP+RT or Cryo+RT. Urethral complication was defined as development of stenosis, fistula, or having a urethral/bladder neck procedure>30 days after treatment (excluding TURP, diagnostic procedures, anti-incontinence surgery). We compared the incidence of urethral complications among different groups. RESULTS: 47,387 patients with PC diagnosis were identified with a median age of 66 years. 3,140 patients developed a urethral complication at a median follow-up of 76 months. Table 1 shows the cumulative incidence of urethral complications. On Kaplan-Meier analysis (Figure 1), urethral complication rate was different among groups (p<0.001). On multivariable Cox-regression including age (p<0.001) and stage (p=0.002), all treatment modalities (except for Cryo) were associated with development of urethral complications compared to MT/O. In particular, combined modalities such as RP+RT (HR 2.9; p<0.001) and RT+Cryo (HR 3.6; p<0.001) showed the highest risk. Complications after RP reached a plateau at 10 years whereas other modalities continued to accumulate complications over the long-term. CONCLUSIONS: Patients undergoing prostate cancer treatment are at risk developing urethral complications in the long-term regardless of treatment type. Specifically, combined modalities pose a heightened risk for developing these complications. Download PPT Source of Funding: Not applicable © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e516 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Carlos I. Calvo More articles by this author Keith F. Rourke 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)0.004
metaresearch head score (Gemma)0.012
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 score0.194
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.023
GPT teacher head0.349
Teacher spread0.326 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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