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Record W2900705575 · doi:10.5489/cuaj.5455

30-day readmission after radical cystectomy: Identifying targets for improvement using the phases of surgical care

2018· article· en· W2900705575 on OpenAlexvenueno aff
Ian Berger, Leilei Xia, Christopher Wirtalla, Phillip Dowzicky, Thomas J. Guzzo, Rachel R. Kelz

Bibliographic record

VenueCanadian Urological Association Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCystectomyOdds ratioCOPDComplicationConfidence intervalBody mass indexOverweightSurgeryBladder cancerLogistic regressionInternal medicineCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: Postoperative readmissions following radical cystectomy (RC) have gained attention in the past decade. Postoperative and post-discharge complications play a role in readmission rates; however, our ability to predict readmissions remains poor. METHODS: Using the National Surgical Quality Improvement Program database, we identified patients with bladder cancer undergoing RC from 2013-2015. Complications were defined as postoperative and post-discharge. Outcomes were 30-day readmission, post-discharge complications, and post-discharge major complications. Patient, operative, and complication factors were assessed using multivariable logistic regression. RESULTS: We identified 4457 patients who underwent RC; 9.2% of patients experienced a postoperative complication, 18.8% experienced a post-discharge complication, and 20.3% were readmitted. Overweight and obese body mass index (BMI), dependent functional status, chronic obstructive pulmonary disease (COPD), a continent diversion, and duration of operation were associated with post-discharge complications. Postoperative complications were not associated with post-discharge complications. Readmission was associated with Black race (odds ratio [OR] 1.5; 95% confidence interval [CI] 1.0-2.1), overweight (OR 1.5; 95% CI 1.2-1.8) and obese BMI (OR 1.5; 95% CI 1.2-1.9), diabetes (OR 1.2; 95% CI 1.0-1.5), COPD (OR 1.4; 95% CI 1.0-1.8), steroid use (OR 1.5; 95% CI 1.0-2.2), a continent diversion (OR 1.4; 95% CI 1.1-1.7), duration of operation (OR 1.1; 95% CI 1.1-1.2), and postoperative complications (OR 1.5; 95% CI 1.2-2.0). The majority of readmissions experienced a post-discharge complication. CONCLUSIONS: Factors that span the preoperative, intraoperative, postoperative, and post-discharge phases of care were identified to increase readmission risk. To improve readmission rates, interventions will have to target factors across the surgical experience.

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)0.003
metaresearch head score (Gemma)0.016
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.301
Teacher spread0.278 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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