PD63-09 UTILIZATION OF PSYCHIATRIC RESOURCES PRIOR TO GENITOURINARY (GU) CANCER DIAGNOSIS: IMPLICATIONS FOR SURVIVAL OUTCOMES
Bibliographic record
Abstract
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 ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".