PD63-09 UTILIZATION OF PSYCHIATRIC RESOURCES PRIOR TO GENITOURINARY (GU) CANCER DIAGNOSIS: IMPLICATIONS FOR SURVIVAL OUTCOMES
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Résumé
You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Practice Patterns, Quality of Life and Shared Decision Making IV (PD63)1 Apr 2019PD63-09 UTILIZATION OF PSYCHIATRIC RESOURCES PRIOR TO GENITOURINARY (GU) CANCER DIAGNOSIS: IMPLICATIONS FOR SURVIVAL OUTCOMES Zachary Klaassen*, Christopher J. D. Wallis, Hanan Goldberg, Thenappan Chandrasekar, Rashid K. Sayyid, Stephen B. Williams, Kelvin A. Moses, Martha K. Terris, Robert K. Nam, Paul Kurdyak, and Girish S. Kulkarni Zachary Klaassen*Zachary Klaassen* More articles by this author , Christopher J. D. WallisChristopher J. D. Wallis More articles by this author , Hanan GoldbergHanan Goldberg More articles by this author , Thenappan ChandrasekarThenappan Chandrasekar More articles by this author , Rashid K. SayyidRashid K. Sayyid More articles by this author , Stephen B. WilliamsStephen B. Williams More articles by this author , Kelvin A. MosesKelvin A. Moses More articles by this author , Martha K. TerrisMartha K. Terris More articles by this author , Robert K. NamRobert K. Nam More articles by this author , Paul KurdyakPaul Kurdyak More articles by this author , and Girish S. KulkarniGirish S. Kulkarni More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000557378.00047.93AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: There is emerging evidence that oncology patients with pre-existing mental illness may have poorer survival compared to patients without psychiatric disease. Furthermore, cancer diagnosis may be associated with an increased risk of suicide. However, studies published thus far have failed to account for utilization of psychiatric resources, which may confound this relationship. The objective of this study was to (i) assess the impact of psychiatric utilization (PU) prior to cancer diagnosis on cancer-specific mortality (CSM), and (ii) to assess the effect of cancer diagnosis on suicide risk compared to the general population, accounting for pre-diagnosis PU. METHODS: All residents of Ontario, Canada diagnosed with either prostate, bladder or kidney cancer (1997-2014) were included. Each patient was assigned a psychiatric utilization gradient (PUG) score in the five years prior to cancer diagnosis: 0 (none), 1 (outpatient), 2 (emergency department), 3 (hospital admission). First, a multivariable cause-specific hazard model was used to assess the effect of PUG score on CSM. Second, non-cancer controls were matched 4:1 to cancer patients based on sociodemographic variables and a marginal cause-specific hazard model was used to assess the effect of cancer on the risk of suicidal death. RESULTS: 191,068 patients were included (137,699 prostate, 29,884 bladder, 23,485 kidney cancer): 109,154 (57.1%) with PUG score 0, 79,553 (41.6%) PUG score 1, 1,596 (0.84%) PUG score 2, and 765 (0.40%) PUG score 3. Increasing pre-diagnosis PU was associated with increased CSM: HR 1.78 (95%CI 1.47-2.14) among patients with PUG score 3 (vs 0) and HR 1.14 (95%CI 0.99-1.32) among those with PUG score 2. These patients with GU malignancies were then matched to 528,387 controls without any cancer diagnosis. Patients with GU cancer had a higher risk of dying of suicide compared to controls (HR 1.16, 95%CI 1.00-1.36). Specifically, among individuals with PUG score 0, those with cancer were significantly more likely to die of suicide compared to patients without cancer (HR 1.39, 95%CI 1.12-1.74). CONCLUSIONS: Pre-cancer diagnosis PU is associated with worse CSM following diagnosis among patients with GU malignancies, with a graded effect. Additionally, the cancer diagnosis confers an increased risk of suicide, compared to the general population, even after accounting for pre-cancer diagnosis PU. Source of Funding: CUOG-CUA-Astellas Augusta, GA; Toronto, Canada; Philadelphia, PA; Augusta, GA; Galveston, TX; Nashville, TN; Augusta, GA; Toronto, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e1115-e1116 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Zachary Klaassen* More articles by this author Christopher J. D. Wallis More articles by this author Hanan Goldberg More articles by this author Thenappan Chandrasekar More articles by this author Rashid K. Sayyid More articles by this author Stephen B. Williams More articles by this author Kelvin A. Moses More articles by this author Martha K. Terris More articles by this author Robert K. Nam More articles by this author Paul Kurdyak More articles by this author Girish S. Kulkarni 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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,038 | 0,005 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».