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Partial vs. radical nephrectomy in non-metastatic pT3a kidney cancer patients: a population-based study

2022· article· en· W4211212023 on OpenAlexaff
Angela Pecoraro, Daniele Amparore, Matteo Manfredi, Federico Piramide, Enrico Checcucci, Zhe Tian, D. Peretti, Cristian Fiori, Pierre I. Karakiewicz, Francesco Porpiglia

Bibliographic record

VenueMinerva Urology and Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNephrectomyMedicinePropensity score matchingUrologyUrothelial cancerPopulationIncidence (geometry)Cumulative incidenceKidney cancerInternal medicineSurgeryCancerKidneyBladder cancer

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to test for differences in cancer specific mortality (CSM) rates between radical nephrectomy (RN) and partial nephrectomy (PN) in pT3a nmRCC patients. METHODS: Within the surveillance, epidemiology, and end results database (2005-2016), 13,177 pT3a patients treated with either PN or RN were identified. Before and after 1:2 ratio propensity score (PS)-match between PN and RN patients, cumulative incidence plot and competing risks regression (CRR) were used to test differences in CSM and other cause mortality (OCM) rates. RESULTS: Relative to PN (N.=1615, 22.5%), RN patients harbored higher tumor size (72 vs. 38 mm; >70 mm 51 vs.10%), of more aggressive histology, collecting duct (0.4 vs. 0.2%) and sarcomatoid (2.3 vs.0.8%), of higher grade (51.0 vs. 37.5%). After PS-matching and OCM adjustment, 5-year CSM was 3-fold higher after RN than PN (P<0.01). Similarly, after PS matching and CSM adjustment, also 5-year OCM rates were higher after RN (HR: 1.59, P=0.0003). CONCLUSIONS: PN does not appear to compromise the oncological outcomes in patients with pT3a or high-grade renal masses when compared with RN. Therefore, these concerns should not deter a surgeon from attempting PN when otherwise technically feasible.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.268
Teacher spread0.255 · 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 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

Citations15
Published2022
Admission routes1
Has abstractyes

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