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Record W2330417965 · doi:10.1016/j.juro.2016.02.1727

MP75-09 THE NATURAL HISTORY OF RENAL FUNCTION AFTER SURGICAL MANAGEMENT OF RENAL CELL CARCINOMA: RESULTS FROM THE CANADIAN KIDNEY CANCER INFORMATION SYSTEM

2016· article· en· W2330417965 on OpenAlexaboutno aff
Ross Mason, Anil Kapoor, Zhihui Liu, Olli Saarela, Simon Tanguay, Michael A.S. Jewett, Antonio Finelli, Louis Lacombe, Jun Kawakami, Ronald B. Moore, Christopher Morash, Peter C. Black, Ricardo Rendon

Bibliographic record

VenueThe Journal of Urology · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNephrectomyRenal cell carcinomaRenal functionKidney cancerCancerNatural historyKidney diseaseKidneyInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyKidney Cancer: Localized: Surgical Therapy V1 Apr 2016MP75-09 THE NATURAL HISTORY OF RENAL FUNCTION AFTER SURGICAL MANAGEMENT OF RENAL CELL CARCINOMA: RESULTS FROM THE CANADIAN KIDNEY CANCER INFORMATION SYSTEM Ross Mason, Anil Kapoor, Zhihui Liu, Olli Saarela, SImon Tanguay, Michael Jewett, Antonio Finelli, Louis Lacombe, Jun Kawakami, Ronald Moore, Chris Morash, Peter Black, and Ricardo Rendon Ross MasonRoss Mason , Anil KapoorAnil Kapoor , Zhihui LiuZhihui Liu , Olli SaarelaOlli Saarela , SImon TanguaySImon Tanguay , Michael JewettMichael Jewett , Antonio FinelliAntonio Finelli , Louis LacombeLouis Lacombe , Jun KawakamiJun Kawakami , Ronald MooreRonald Moore , Chris MorashChris Morash , Peter BlackPeter Black , and Ricardo RendonRicardo Rendon View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.1727AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Patients who undergo surgical management of renal cell carcinoma (RCC) are at risk for chronic kidney disease due to loss of nephrons and intraoperative ischemic injury. This decrease in renal function is associated with both increased morbidity and mortality. This study explored factors associated with decreased renal function in patients undergoing partial or radical nephrectomy. METHODS Patients who underwent partial or radical nephrectomy were identified from the Canadian Kidney Cancer Information System (CKCIS), a large multiinstitutional prospectively maintained database. Univariable and multivariable logistic regression were used to determine the association between post-operative estimated glomerular filtration rate (eGFR) and type or surgery (radical versus partial), duration of ischemia, ischemia type (warm versus cold), and tumor size. RESULTS With a median follow-up of 26 months, 1379 patients were identified from the CKCIS database including 665 and 714 who underwent partial and radical nephrectomy, respectively. Patients undergoing radical nephrectomy had a lower eGFR at 3, 12, and 24 months post-operatively with the mean eGFR at 3 months being 19 ml/min/1.73m2 lower than those undergoing partial nephrectomy (p<0.001). A lower pre-operative eGFR and increasing age were also associated with a lower eGFR post-operatively (p<0.01) in both the entire cohort and among patients undergoing partial nephrectomy. Severe renal failure (Stage 4 or 5) developed post-operatively in 31.0%, 5.4%, and 1.1% of patients with pre-operative stage 1, 2, or 3 chronic kidney disease. Among patients undergoing partial nephrectomy, ischemia type and duration were not predictive of post-operative decline in eGFR at all time intervals. CONCLUSIONS Patients undergoing radical nephrectomy have a greater long-term reduction in renal function compared with those undergoing partial nephrectomy. In this modern series with generally short ischemia durations, ischemia duration and type were not predictive of post-operative renal function. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e981-e982 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Ross Mason More articles by this author Anil Kapoor More articles by this author Zhihui Liu More articles by this author Olli Saarela More articles by this author SImon Tanguay More articles by this author Michael Jewett More articles by this author Antonio Finelli More articles by this author Louis Lacombe More articles by this author Jun Kawakami More articles by this author Ronald Moore More articles by this author Chris Morash More articles by this author Peter Black More articles by this author Ricardo Rendon 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)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.013
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.010

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 head0.012
GPT teacher head0.200
Teacher spread0.189 · 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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Citations1
Published2016
Admission routes1
Has abstractyes

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