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Association of partial nephrectomy and presence of robotic surgery for kidney cancer in the United States.

2014· article· en· W2589696161 on OpenAlexaff
Steven V. Kardos, Brian Shuch, Peter G. Schulam, Quoc‐Dien Trinh, Maxine Sun, Nathan D. Shippee, Jesse D. Sammon, Simon P. Kim

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineNephrectomyRenal cell carcinomaKidney cancerLogistic regressionOdds ratioCohortSurgeryCancerInternal medicinePopulationUrologyKidney

Abstract

fetched live from OpenAlex

484 Background: While hospital and surgeon characteristics are associated with the type of nephrectomy performed for renal cell carcinoma (RCC), it is unknown whether hospital presence of robotic surgery increases the likelihood of patients receiving partial nephrectomy (PN). Therefore, we evaluate the relationship of PN and hospital presence of robotic surgery from a population-based cohort in the U.S. Methods: After merging the Nationwide Inpatient Sample (NIS) and the American Hospital Association (AHA) survey from 2006 to 2008, we identified 21,999 patients who underwent either PN or radical nephrectomy (RN) for RCC. The primary outcome of this study was the type of nephrectomy performed. Multivariable logistic regression was used to identify hospital characteristics associated with receipt of PN, after adjusting for patient case mix. Results: Overall, we identified 4,832 (22.0%) and 16,347 (88.0%) patients who were surgically treated for RCC with PN and RN, respectively. On multivariable analysis, patients undergoing surgery were more likely to receive PN at academic (OR: 2.77;p<0.001), urban (OR: 3.66; p<0.001), and American College of Surgeon (ACOS) designated cancer centers (OR: 1.10; p<0.05) compared to non-academic, rural, and non-designated hospitals, respectively. After adjusting for patient and hospital characteristics, patients undergoing surgery at hospitals with presence of robotic surgery were also associated with higher adjusted odds ratios for receipt of PN compared to those treated at hospitals without the presence of this advanced treatment technology (OR: 1.28; p<0.001). Conclusions: While academic status and urban locations are established characteristics influencing the type of nephrectomy performed for RCC, ACOS cancer center designation and hospital presence of robotic surgery were also associated with higher use of PN. Our results are informative in identifying key hospital characteristics which may facilitate greater adoption of PN.

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)0.002
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.121
GPT teacher head0.436
Teacher spread0.315 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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