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Record W1969447092 · doi:10.1016/j.juro.2015.02.2495

MP67-12 THE MODIFIED FRAILTY INDEX AS A MARKER OF ADVERSE OUTCOMES DURING CYSTECTOMY FOR UROTHELIAL CANCER

2015· article· en· W1969447092 on OpenAlexaboutno aff
Max Kates, Hiten D. Patel, Gregory Joice, Jeffrey J. Tosoian, Nikolai A. Sopko, Jen‐Jane Liu, Phillip M. Pierorazio, Trinity J. Bivalacqua

Bibliographic record

VenueThe Journal of Urology · 2015
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCystectomyMedicineBladder cancerUrothelial cancerIndex (typography)Lung cancerPopulationFrailty IndexAdverse effectGerontologyCancerInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyBladder Cancer: Invasive IV1 Apr 2015MP67-12 THE MODIFIED FRAILTY INDEX AS A MARKER OF ADVERSE OUTCOMES DURING CYSTECTOMY FOR UROTHELIAL CANCER Max Kates, Hiten Patel, Gregory Joice, Jeffrey Tosoian, Nikolai Sopko, Jen-Jane Liu, Phillip Pierorazio, and Trinity Bivalacqua Max KatesMax Kates More articles by this author , Hiten PatelHiten Patel More articles by this author , Gregory JoiceGregory Joice More articles by this author , Jeffrey TosoianJeffrey Tosoian More articles by this author , Nikolai SopkoNikolai Sopko More articles by this author , Jen-Jane LiuJen-Jane Liu More articles by this author , Phillip PierorazioPhillip Pierorazio More articles by this author , and Trinity BivalacquaTrinity Bivalacqua More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2495AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Frailty has been identified as a marker of physiologic reserve, and a more accurate predictor of adverse postoperative outcomes compared with age. Although many definitions of frailty exist, recently a clinical predictive rule, the “modified frailty index”(mFI), has been developed utilizing administrative data to predict adverse outcomes in the lung cancer population undergoing lobectomy. Our goal was to validate this clinical rule among patients with bladder cancer undergoing cystectomy. METHODS Patients undergoing cystectomy were identified from the National Surgical Quality Improvement Program (NSQIP) participant use files (2006-2011). The mFI was defined as in prior studies with 11 variables based on mapping the Canadian Study of Health and Aging Frailty Index to NSQIP comorbidities and activities of daily living (ADL)s. These 11 variables each received 1 point, and the sum was divided by 11 for a fraction between 0 and 1. Univariate, χ2, independent sample t-test, and logistic regression analyses were performed where appropriate. RESULTS Of the 1302 cystectomy patients identified, 30% had mFI of 0, 40% had mFI of 0.09, 21% had mFI of 0.18, and 9% had mFI ≥0.27. Overall, 56% of patients experienced a Clavien complication. Patients with mFI ≥0.27 were older ( 72 vs 64 yrs)and more likely to be smokers (54%) compared with mFI of 0 (30%, p<0.01). Mean operative times (342-349 minutes) were similar across mFI indices. Reoperation (5% vs 8.5%) and readmission (20.5% vs 25%) were higher when mFI =0 compared with mFI≥0.27 (P<0.01). Clavien 4 and above complications occurred in 9.1% (36/396), 10.1% (53/526), 12.9 % (35/270) and 13.6% (15/110) among patients with an mFI of 0, 0.09, 0.18, and ≥0.27, respectively (p=0.05). Similarly, the overall mortality rate increased from 2.5% in the lowest frailty index group to 5.4% in the highest. CONCLUSIONS Among patients undergoing cystectomy, the modified frailty index can identify those patients at greater risk for severe complications, readmissions, and mortality. Given that bladder cancer is increasing in prevalence particularly among the elderly, pre-operative risk stratification is crucial to inform decision-making. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e854 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Max Kates More articles by this author Hiten Patel More articles by this author Gregory Joice More articles by this author Jeffrey Tosoian More articles by this author Nikolai Sopko More articles by this author Jen-Jane Liu More articles by this author Phillip Pierorazio More articles by this author Trinity Bivalacqua 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)0.001
metaresearch head score (Gemma)0.006
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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.049
GPT teacher head0.333
Teacher spread0.284 · 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
Published2015
Admission routes1
Has abstractyes

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