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Record W3180312779 · doi:10.1016/s0302-2838(21)01586-4

The effect of lymph node dissection on cancer-specific survival in salvage radical prostatectomy patients

2021· article· en· W3180312779 on OpenAlexaff
Mike Wenzel, Christoph Würnschimmel, Luigi Nocera, C. Collà Ruvulo, Shahrokh F. Shariat, Alberto Briganti, Markus Graefen, Luis A. Kluth, Philipp Mandel, Felix Chun, Pierre I. Karakiewicz

Bibliographic record

VenueEuropean Urology · 2021
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsMedicineProstatectomyDissection (medical)Lymph nodeProstate cancerUrologySurgerySalvage therapyCancerInternal medicineChemotherapy

Abstract

fetched live from OpenAlex

Background: We hypothesized that lymph node dissection (LND) at salvage radical prostatectomy may be associated with lower cancer-specific mortality (CSM) and we tested this hypothesis.Methods: We relied on surveillance, epidemiology, and end results (2004-2016) to identify all salvage radical prostatectomy patients.Categorical, as well as univariate and multivariate Cox regression models tested the effect of LND (LND performed vs. not), as well as at its extent (log-transformed lymph node count) on CSM.Results: Of 427 salvage radical prostatectomy patients, 120 (28.1%) underwent LND with a median lymph node count of 6 (interquartile range [IQR], 3-11).According to LND status, no significant or clinically meaningful differences were recorded in PSA at diagnosis, stage and biopsy Gleason score at diagnosis, except for age at prostate cancer diagnosis (LND performed 63 vs. 68 years LND not

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)0.004
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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