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Record W2903066048 · doi:10.1515/hmbci-2018-0049

Impact of adipose tissue on prostate cancer aggressiveness – analysis of a high-risk population

2018· article· en· W2903066048 on OpenAlexaff
Guila Delouya, David Tiberi, Sahir Bhatnagar, Shanie Campeau, Fred Saad, Daniel Taussky

Bibliographic record

VenueHormone Molecular Biology and Clinical Investigation · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversité de MontréalJewish General HospitalMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsAdipose tissueMedicineProstate cancerProstatectomyUnivariate analysisInternal medicineProstateCancerLogistic regressionOncologyMultivariate analysisUrology

Abstract

fetched live from OpenAlex

Background We investigated whether visceral adiposity is associated with more aggressive disease at prostatectomy. Materials and methods Four hundred and seventy-four patients referred for postoperative adjuvant or salvage radiotherapy were included in this study. Primary endpoints were positive surgical margins (pSM) or extracapsular extension (ECE). Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were manually contoured. Univariate and multivariate logistic regression was performed. Results In univariate analysis, VAT volume (p = 0.006), adipose tissue ratio (VAT/SAT, p = 0.003), density of the SAT (p = 0.04), as well as age (p < 0.001) were associated with pSM. In the univariate analysis, SAT density was associated with a trend towards a higher rate of ECE (p = 0.051) but visceral fat volume (p = 0.01), as well as the adipose tissue ratio (p = 0.03) were both protective factors. None of the adipose tissue measurements or BMI had an influence on biochemical recurrence or overall survival (all p ≥ 0.5). Conclusions SAT-volume and increased SAT-density were generally associated with more aggressive prostate cancers whereas VAT as a protective factor. These findings emphasize a possible mechanism for the association between obesity and prostate cancer aggressiveness.

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 score0.031
Threshold uncertainty score0.463

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 studies0.0000.000
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 head0.025
GPT teacher head0.397
Teacher spread0.372 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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