MétaCan
Menu
← Back to cohort

PD30-03 PSYCHOLOGICAL MORBIDITY ASSOCIATED WITH A NEW DIAGNOSIS OF PROSTATE CANCER: RATES AND PREDICTORS OF DEPRESSIVE SYMPTOMS IN THE RADICAL PC STUDY

2019· article· en· W2942249002 on OpenAlexaboutno aff
Gagan Fervaha, Jason Izard, Dean A. Tripp, Selina Rajan, Sarah Karampatos, Bobby Shayegan, Edward D. Matsumoto, Tamim Niazi, Annabel Chen‐Tournoux, Vincent Fradet, Yves Fradet, Guila Delouya, Daniel Taussky, Luke T. Lavallée, Christopher Johnson, Joseph L. Chin, Darin Gopaul, Margot Davis, J.H. Pinthus, Darryl P. Leong, Robert Siemens

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychoanalysisMedicinePsychology

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyProstate Cancer: Epidemiology & Natural History I (PD30)1 Apr 2019PD30-03 PSYCHOLOGICAL MORBIDITY ASSOCIATED WITH A NEW DIAGNOSIS OF PROSTATE CANCER: RATES AND PREDICTORS OF DEPRESSIVE SYMPTOMS IN THE RADICAL PC STUDY Gagan Fervaha*, Jason Izard, Dean Tripp, Selina Rajan, Sarah Karampatos, Bobby Shayegan, Edward Matsumoto, Tamim Niazi, Annabel Chen-Tournoux, Vincent Fradet, Yves Fradet, Guila Delouya, Daniel Taussky, Luke Lavallee, Christopher Johnson, Joseph Chin, Darin Gopaul, Margot Davis, Jehnonathan Pinthus, Darryl Leong, and Robert Siemens Gagan Fervaha*Gagan Fervaha* More articles by this author , Jason IzardJason Izard More articles by this author , Dean TrippDean Tripp More articles by this author , Selina RajanSelina Rajan More articles by this author , Sarah KarampatosSarah Karampatos More articles by this author , Bobby ShayeganBobby Shayegan More articles by this author , Edward MatsumotoEdward Matsumoto More articles by this author , Tamim NiaziTamim Niazi More articles by this author , Annabel Chen-TournouxAnnabel Chen-Tournoux More articles by this author , Vincent FradetVincent Fradet More articles by this author , Yves FradetYves Fradet More articles by this author , Guila DelouyaGuila Delouya More articles by this author , Daniel TausskyDaniel Taussky More articles by this author , Luke LavalleeLuke Lavallee More articles by this author , Christopher JohnsonChristopher Johnson More articles by this author , Joseph ChinJoseph Chin More articles by this author , Darin GopaulDarin Gopaul More articles by this author , Margot DavisMargot Davis More articles by this author , Jehnonathan PinthusJehnonathan Pinthus More articles by this author , Darryl LeongDarryl Leong More articles by this author , and Robert SiemensRobert Siemens More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556135.36362.37AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Across all cancer sites and stages, prostate cancer has one of the greatest median 5-year survival rates. With this comes a focus on survivorship issues following diagnosis and treatment. In the current study we sought to evaluate the prevalence and predictors of depressive symptoms in a large, contemporary, prospectively collected sample of newly diagnosed men with prostate cancer. METHODS: Data from the current study were drawn from the RADICAL PC study, a parent prospective cohort study conducted across 13 sites in Canada. Men with a diagnosis of prostate cancer within 12 months were recruited (2017-ongoing). Depressive symptoms were evaluated using the 9 item version of the Patient Health Questionnaire (PHQ-9). A score of 8 or higher on this scale represents clinically relevant depressive symptoms. To evaluate predictors of depressive symptoms, a logistic regression model was constructed including biological, psychological, and social predictor variables. RESULTS: Data from 1440 patients were available at the time of this analysis. Of these, 108 (7.5%) endorsed clinically significant burden of depressive symptoms. Having a pre-existing diagnosis of depression or anxiety disorder increased risk of depressive symptoms at the time of evaluation (OR=4.12, p<0.001). Above and beyond this, greater comorbid conditions (OR=1.20, p=0.03), poorer functional status (OR=5.78, p<0.001), and smoking (OR=3.27, p=0.001) also predicted depressive symptoms. Higher education (OR=0.48, p=0.03) and being retired (OR=0.57, p=0.04) were protective against depression. Despite having univariate associations with depression, stage of disease and income did not have independent predictive value in our multivariate model. CONCLUSIONS: Our multi-centre study of newly diagnosed prostate cancer patients confims the presence of clinically significant depressive symptoms in a contemporary and sizeable sample of men. Early in their cancer trajectory, men with prostate cancer are burdened by not only the extent of their illness but also by many other interacting variables, some modifiable and others not. Clinicians should be vigilant to screen for depression in those patients with poor social determinants of health and concomitant disability. Source of Funding: Prostate Cancer Canada Kingston, Canada; Hamilton, Canada; Hamiilton, Canada; Montreal, Canada; Quebec City, Canada; Montreal, Canada; Ottawa, Canada; London, Canada; Kitchener, Canada; Vancouver, Canada; Hamilton, Canada; Kingston, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e560-e560 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Gagan Fervaha* More articles by this author Jason Izard More articles by this author Dean Tripp More articles by this author Selina Rajan More articles by this author Sarah Karampatos More articles by this author Bobby Shayegan More articles by this author Edward Matsumoto More articles by this author Tamim Niazi More articles by this author Annabel Chen-Tournoux More articles by this author Vincent Fradet More articles by this author Yves Fradet More articles by this author Guila Delouya More articles by this author Daniel Taussky More articles by this author Luke Lavallee More articles by this author Christopher Johnson More articles by this author Joseph Chin More articles by this author Darin Gopaul More articles by this author Margot Davis More articles by this author Jehnonathan Pinthus More articles by this author Darryl Leong More articles by this author Robert Siemens More articles by this author Expand All Advertisement PDF downloadLoading ...

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)0.003
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.309
Teacher spread0.289 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Urology→Same topicProstate Cancer Diagnosis and Treatment→French-language works237,207→