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Record W2341616996 · doi:10.1016/j.juro.2016.01.122

Low Other Cause Mortality Rates Reflect Good Patient Selection in Patients with Prostate Cancer Treated with Radical Prostatectomy

2016· article· en· W2341616996 on OpenAlexaff
Katharina Böehm, Alessandro Larcher, Zhe Tian, Philipp Mandel, Jonas Schiffmann, Pierre I. Karakiewicz, Markus Graefen, Hartwig Huland, Derya Tilki

Bibliographic record

VenueThe Journal of Urology · 2016
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsMedicineProstatectomyProstate cancerUrologySelection (genetic algorithm)ProstateOncologyCancerGynecologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Treatment decisions in patients with prostate cancer are affected by patient age regardless of higher life expectancy compared to the baseline population. Our aim was to quantify cancer specific and other cause mortality rates after radical prostatectomy. MATERIALS AND METHODS: A total of 8,741 patients with prostate cancer underwent radical prostatectomy between 1992 and 2009 at a European center. Ten-year other cause and cancer specific mortality rates were determined by age and comorbidities, and age and Cancer of the Prostate Risk Assessment Post-Surgical (CAPRA-S) risk groups. Competing risk regression was used for risk factor analyses including clinical and pathological variables. RESULTS: Ten-year other cause mortality rates increased with patient age, including 4.8%, 9.8%, 13.6% and 16.5% in men younger than 60, 60 to 64, 65 to 69 and 70 years or older, respectively. Cancer specific mortality was the leading cause of death in CAPRA-S high risk cases regardless of age. On multivariate analyses age groups achieved independent predictor status for other cause mortality (ages 60 to 64 years HR 1.81, 95% CI 1.26-2.62, 65 to 69 years HR 2.48, 95% CI 1.73-3.56 and 70 years or greater HR 3.02, 95% CI 1.97-4.62) as well as Charlson comorbidity indexes 1 (HR 1.45, 95% CI 1.00-2.09) and 3 or greater (HR 3.99, 95% CI 1.57-10.1). Gleason score 3 + 4 and 4 + 3 or greater, pT3b stage, lymph node invasion and positive margin status achieved independent predictor status when the end point was cancer specific mortality. The CAPRA-S high risk constellation increased cancer specific mortality risk in multifold fashion (HR 26, 95% CI 16-56). CONCLUSIONS: In patients with the CAPRA-S high risk constellation the rate of cancer specific mortality increased in multifold fashion and contributed to most deaths regardless of patient age. Low other cause mortality rates in all age groups showed reasonable patient selection.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.000
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 head0.012
GPT teacher head0.279
Teacher spread0.267 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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