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Record W4388692269 · doi:10.1097/ju9.0000000000000071

Functional Follow-Up After Cystectomy and Urinary Diversion: A Narrative Review

2023· review· en· W4388692269 on OpenAlexaff
Ernest Kaufmann, Peter C. Black, James W.F. Catto, Hooman Djaladat, Saum Ghodoussipour, Jill Hamilton‐Reeves, Bente Thoft Jensen, Wassim Kassouf, Susanne Vahr Lauridsen, Seth P. Lerner, Carlos Llorente, Katherine Loftus, Ilaria Lucca, Alberto Martini, Mark A. Preston, Sarah P. Psutka, John P. Sfakianos, Jay B. Shah, Marian S. Wettstein, Stephen B. Williams, Siamak Daneshmand, Christian D. Fankhauser

Bibliographic record

VenueJU Open Plus · 2023
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsUrinary diversionCystectomyNarrativeUrinary systemMedicineUrologyGeneral surgeryArtInternal medicineBladder cancerLiteratureCancer

Abstract

fetched live from OpenAlex

Abstract Introduction: Follow-up after urinary diversion aims to detect functional complications to prevent harm and improve quality of life. Methods: We conducted a literature search and reviewed guidelines and institutional follow-up protocols. Results: We included 14 studies providing data of 3282 patients. Functional complications can be seen in up to 90% of all patients within 15 years after urinary diversion and mainly include impairment of urinary or sexual function as well as renal/metabolic disturbances, but only limited evidence supporting any functional follow-up recommendation was identified. Current guideline recommendation should be rephrased to ensure routine implementation of functional follow-up investigation. Discussion: Future research is required to assess whether, which, and how follow-up protocols after cystectomy affect functional results to inform optimal surveillance procedures after treatment. Patient Summary: In this review of recommended follow-up protocols after cystectomy, we observed different recommendations and discuss future research areas.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.116
GPT teacher head0.382
Teacher spread0.266 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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