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Record W4390976336 · doi:10.3390/medicina60010173

Cardiovascular Disease and Chronic Pulmonary Disease Increase the Risk of Short-Term Major Postoperative Complications after Robotic-Assisted Radical Prostatectomy

2024· article· en· W4390976336 on OpenAlexaff
Carolin Siech, Antonia Gruber, Mike Wenzel, Clara Humke, Pierre I. Karakiewicz, Luis A. Kluth, Felix K.‐H. Chun, Benedikt Hoeh, Philipp Mandel

Bibliographic record

VenueMedicina · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineProstatectomyOdds ratioLogistic regressionSurgeryDiseaseConcomitantPulmonary diseaseProstate cancerInternal medicineCancer

Abstract

fetched live from OpenAlex

Background and objectives: Certain comorbidities may be associated with a higher risk of complications after robotic-assisted radical prostatectomy. Material and Methods: Relying on a tertiary care database, we identified robotic-assisted radical prostatectomy patients (January 2014–March 2023). Short-term major postoperative complications were defined according to Clavien Dindo as ≥IIIa within 30 days after robotic-assisted radical prostatectomy. Results: Of 1148 patients, the rates of postoperative Clavien Dindo IIIa, Clavien Dindo IIIb, Clavien Dindo IVa, and Clavien Dindo IVb complications were 3.3%, 1.4%, 0.3%, and 0.2%, respectively. Of those, 28 (47%) had lymphoceles, and 8 (13%) had bleeding-associated complications. Patients with cardiovascular disease (8 vs. 4%) or chronic pulmonary disease (13 vs. 5%) were more likely to have complications. In multivariable logistic regression models, cardiovascular disease (odds ratio: 1.78; p = 0.046) and chronic pulmonary disease (odds ratio: 3.29; p = 0.007) remained associated with an increased risk of postoperative complications. Conclusions: Complications after robotic-assisted radical prostatectomy are predominantly manageable without anesthesia. Concomitant cardiovascular disease and chronic pulmonary disease were both associated with a higher risk of postoperative complications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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