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Record W3032621023 · doi:10.5489/cuaj.6685

Optimizing screening and management of cardiovascular health in prostate cancer

2020· review· en· W3032621023 on OpenAlexaffvenueabout
Miran Kenk, Jean‐Claude Grégoire, Marc‐André Côté, Kim A. Connelly, Margot K. Davis, George K. Dresser, Nina Ghosh, Shaun G. Goodman, Christopher Johnson, Neil Fleshner

Bibliographic record

VenueCanadian Urological Association Journal · 2020
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of OttawaQueensway-Carleton HospitalStornoway Diamond (Canada)University of British ColumbiaWestern UniversitySt. Michael's HospitalCentre hospitalier universitaire de QuébecMontreal Heart InstitutePrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineProstate cancerDiseaseMultidisciplinary approachMultidisciplinary teamIntensive care medicineAndrogen deprivation therapyIdentification (biology)Clinical PracticeCancerPopulationDisease managementInternal medicinePhysical therapyNursing

Abstract

fetched live from OpenAlex

In clinical practice, cancer management does not consistently encompass screening and identification of cardiovascular (CV) risk. The use of androgen deprivation therapy (ADT) in prostate cancer has been associated with increased CV risk and development of metabolic syndrome, necessitating identification of patients at risk in this population (e.g., those with pre-existing CV disease). A multidisciplinary team of Canadian physicians was assembled to develop a series of recommendations intended to identify patients who may benefit from optimal management of their CV disease and/or modification of cardiac risk factors. A key goal was the development of a simple screening tool for identification of patients with pre-existing CV disease. This simple and inclusive set of recommendations are intended for use within urology clinics to facilitate holistic approaches and simplify the management of patients.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.060
GPT teacher head0.341
Teacher spread0.281 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2020
Admission routes3
Has abstractyes

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