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Enregistrement W2942249002 · doi:10.1097/01.ju.0000556135.36362.37

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 sur 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

Notice bibliographique

RevueThe Journal of Urology · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueProstate Cancer Diagnosis and Treatment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychoanalysisMedicinePsychology

Résumé

récupéré en direct d'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 ...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,060

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,309
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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