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Frailty and adverse clinical outcomes in prostate cancer patients: An analysis from the RADICAL-PC cohort.

2025· article· en· W4407699401 on OpenAlexaff
Rocío Consuelo Baro Vila, Jehonathan H. Pinthus, Sarah Karampatos, Cristina Cano Garcia, Rajibul Mian, José López‐López, Ariel Galapo Kann, Philippe D. Violette, Surya Y Prakash, Vincent Fradet, Patrick Anderson, Nicolás Villareal Trujillo, Li Ling Tan, Robert Sabbagh, Luke T. Lavallée, Lívia Oliveira, Darryl P. Leong

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsOttawa HospitalUniversity of OttawaNiagara Health SystemPopulation Health Research InstituteUniversité LavalHamilton Health SciencesImpactUniversité de SherbrookeMcMaster University
Fundersnot available
KeywordsMedicineProstate cancerCohortAdverse effectOncologyInternal medicineCancerGynecology

Abstract

fetched live from OpenAlex

80 Background: As a disease of older individuals, prostate cancer (PC) may feature physical frailty. Most related studies are retrospective and focus on short-term surgical outcomes. Methods: We conductedan analysis of a prospective study of PC patients either diagnosed within the past year, treated with androgen deprivation therapy (ADT) for the first time within the past six months, or scheduled to initiate ADT within a month . Frailty was assessed using Fried's criteria which includes five domains [Handgrip strength, gait speed, physical activity, unintentional weight loss (>4 kg/year) and exhaustion (≥3 days/week)]. Patients were classified as frail (≥3 criteria), pre-frail (1-2 criteria), or robust (0 criteria). Participants were followed annually to ascertain the occurrence of mortality, new metastases, major adverse cardiovascular events (MACE: myocardial infarction, stroke, cardiovascular death, stroke, heart failure, peripheral arterial disease, venous thromboembolism or arterial revascularization) and hospitalization. The relationship between frailty and these events was evaluated by Cox proportional hazards models adjusted for age at enrolment, education, race, tobacco and alcohol use, diabetes, past history of cardiovascular disease, estimated glomerular filtration rate, PC risk, metastatic disease and ADT exposure. Results: We studied 4304 participants (mean age 69±8 years) from 9 countries: 1429 (33%) were robust, 2394 (56%) pre-frail and 481 (11%), frail. During a median 2.4 years, 336 (8%) died, 161 (4%) died from PC or developed new metastases, 506 (12%) were hospitalized and 262 (6%) experienced MACE. Being prefrail or frail was associated with a higher risk of death, hospitalization and MACE but not PC death or new metastases (Table). Conclusions: Pre-frailty and frailty are common among patients with PC. Frailty is associated with approximately a two-fold increase in mortality, hospitalization or MACE in PC patients independent of a wide range of prognostic factors. Relationship between frailty and clinical outcomes. Characteristic Death Hospitalization PC death or new metastases MACE Hazard ratio (95% confidence interval) p-value Hazard ratio (95% confidence interval) p-value Hazard ratio (95% confidence interval) p-value Hazard ratio (95% confidence interval) p-value Frailty Robust Pre-frail Frail Ref1.54(1.13-2.00)2.90(2.00-4.21) 0.007<0.001 Ref1.39(1.10-1.74)2.29(1.70-3.09) 0.005<0.001 Ref1.05(0.70-1.57)1.47(0.85-2.52) 0.830.17 Ref1.44(1.05-1.97)2.21(1.46-3.34) 0.023<0.001 Estimates are from Cox proportional hazards models adjusted for age at enrolment, education, race, tobacco and alcohol use, diabetes, past history of cardiovascular disease, estimated glomerular filtration rate, PC risk, metastatic disease and ADT exposure.

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)0.002
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.487
Teacher spread0.409 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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