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Record W4408474335 · doi:10.1186/s40959-025-00322-9

Principles of optimal multidisciplinary management of prostate cancer in clinical practice

2025· letter· en· W4408474335 on OpenAlexaffabout
Filipe Cirne, Michiel Sedelaar, Vivek Narayan, Ariane Vieira Scarlatelli Macedo, Anthony Ng, Diogo Assed Bastos, Alberto Briganti, Susan Dent, Nishant Shah, Renato D. Lópes, Daniel J. Lenihan, Darryl P. Leong

Bibliographic record

VenueCardio-Oncology · 2025
Typeletter
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersDuke Clinical Research Institute
KeywordsMultidisciplinary approachProstate cancerClinical PracticeCancerProstateMedicineMedical physicsInternal medicineFamily medicineSociology

Abstract

fetched live from OpenAlex

Advances in the diagnosis and management of prostate cancer have significantly changed the disease landscape. While benefiting from better oncological outcomes, patients are now experiencing higher rates of non-cancer comorbidities, including cardiovascular disease. The increasing impact of cardiovascular disease in those with prostate cancer led to the expanding role of cardio-oncology professionals in enhancing the multidisciplinary care of these patients. As a result, the International Cardio-Oncology Society (IC-OS) launched a 4-webinar series in collaboration with the European Association of Urology and the Canadian Urology Association to inform best practices in the multidisciplinary care of patients with prostate cancer. This program highlighted currently recommended diagnostic and treatment strategies from urology, oncology, and cardiology and emphasized knowledge gaps and future directions. In this article, which is the second in a 2-part series, we review challenging cases that were presented and discussed among a multidisciplinary international panel and highlight ongoing research and future directions from both urology/oncology and cardio-oncology.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.071
GPT teacher head0.460
Teacher spread0.389 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2025
Admission routes2
Has abstractyes

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