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Record W2335536243 · doi:10.1016/j.juro.2012.02.793

709 OLDER AND SICKER RENAL CELL CARCINOMA PATIENTS ARE OPERATED AT LOW VOLUME HOSPITALS

2012· article· en· W2335536243 on OpenAlexaboutno aff
Maxine Sun, Quoc‐Dien Trinh, Marco Bianchi, Jens Hansen, Nawar Hanna, Zhe Tian, Shahrokh F. Shariat, Paul Perrotte, Pierre I. Karakiewicz

Bibliographic record

VenueThe Journal of Urology · 2012
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNephrectomyCharlson comorbidity indexRenal cell carcinomaComorbidityGeneral surgeryInternal medicineKidney

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyKidney Cancer: Evaluation and Staging II1 Apr 2012709 OLDER AND SICKER RENAL CELL CARCINOMA PATIENTS ARE OPERATED AT LOW VOLUME HOSPITALS Maxine Sun, Quoc-Dien Trinh, Marco Bianchi, Jens Hansen, Nawar Hanna, Zhe Tian, Shahrokh Shariat, Paul Perrotte, and Pierre Karakiewicz Maxine SunMaxine Sun Montreal, Canada , Quoc-Dien TrinhQuoc-Dien Trinh Detroit, MI , Marco BianchiMarco Bianchi Milan, Italy , Jens HansenJens Hansen Hamburg, Germany , Nawar HannaNawar Hanna Montreal, Canada , Zhe TianZhe Tian Montreal, Canada , Shahrokh ShariatShahrokh Shariat New York, NY , Paul PerrottePaul Perrotte Montreal, Canada , and Pierre KarakiewiczPierre Karakiewicz Montreal, Canada View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.793AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Hospital volume represents an established determinant of outcomes. We sought to examine the patient selection for surgical candidates according to hospital volume, using patient age and baseline comorbidities as proxies. METHODS Overall, 48172 non-metastatic renal cell carcinoma (RCC) patients were identified amongst 2084 hospitals originating from the Nationwide Inpatient Sample, between years 1998 and 2007. We examined patient age, baseline Charlson comorbidity index (CCI), gender, race, nephrectomy type, hospital teaching status, hospital region, and surgical approach (open vs. laparoscopic) according to hospital volume, which was modeled in a continuously coded fashion. Finally, we examined the effect of hospital volume on patient age and CCI, using linear regression analyses. Adjustment was made for all the aforementioned covariates. RESULTS The overall mean hospital volume was 16 nephrectomies per year (median 9, interquartile range [IQR]: 4–20). First, hospital volume decreased with increasing patient age (≤59 years mean: 17 (median 9) vs. ≥80 years: 13 (median 8), P<0.001) and increasing CCI (0 mean: 16 (median 9) vs. ≥3: 13 (median 8), P<0.001). The effect of hospital volume also differed according to gender, nephrectomy type, hospital teaching status, hospital region, and surgical approach. Specifically, females, patients of Hispanic race, radical nephrectomies, non-teaching hospitals, hospitals located in the West, and cases performed via the open approach were more likely to be of low hospital volume. In univariable linear regression analyses, decreasing age (beta: -0.044, P<0.001) and decreasing CCI (beta: -0.027, P<0.001) was inversely associated with increasing hospital volume. These findings were confirmed in multivariable analyses, where patients with increasing age (beta: -3.512, P<0.001) and higher CCI (beta: -3.896, P<0.001) were more likely to be operated at hospitals with a low hospital volume. CONCLUSIONS Individuals of more advanced age and those with multiple comorbidities tend to be treated at low hospital volume institutions. Such practice may lead to less favorable outcomes in those individuals. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e290-e291 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Maxine Sun Montreal, Canada More articles by this author Quoc-Dien Trinh Detroit, MI More articles by this author Marco Bianchi Milan, Italy More articles by this author Jens Hansen Hamburg, Germany More articles by this author Nawar Hanna Montreal, Canada More articles by this author Zhe Tian Montreal, Canada More articles by this author Shahrokh Shariat New York, NY More articles by this author Paul Perrotte Montreal, Canada More articles by this author Pierre Karakiewicz Montreal, Canada 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)0.000
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.002

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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
Published2012
Admission routes1
Has abstractyes

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