PD07-07 PERSONAL PSA SCREENING AND TREATMENT CHOICES FOR LOCALIZED PROSTATE CANCER AMONG EXPERT PHYSICIANS
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection & Screening II1 Apr 2017PD07-07 PERSONAL PSA SCREENING AND TREATMENT CHOICES FOR LOCALIZED PROSTATE CANCER AMONG EXPERT PHYSICIANS Christopher Wallis, Douglas Cheung, Laurence Klotz, Venu Chalasani, Ricardo Leao, Juan Garisto, Gerard Morton, Robert Nam, Ian Tannock, and Raj Satkunasivam Christopher WallisChristopher Wallis More articles by this author , Douglas CheungDouglas Cheung More articles by this author , Laurence KlotzLaurence Klotz More articles by this author , Venu ChalasaniVenu Chalasani More articles by this author , Ricardo LeaoRicardo Leao More articles by this author , Juan GaristoJuan Garisto More articles by this author , Gerard MortonGerard Morton More articles by this author , Robert NamRobert Nam More articles by this author , Ian TannockIan Tannock More articles by this author , and Raj SatkunasivamRaj Satkunasivam More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.382AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Prostate-specific antigen (PSA) based prostate cancer (PCa) screening and treatment choice for localized PCa remain highly controversial. The physician surrogate method seeks to identify acceptable healthcare interventions by ascertaining the interventions physicians select for themselves. We surveyed urologists, radiation oncologists, and medical oncologists with respect to their personal practices and recommendations to immediate family members regarding PSA screening and the treatment of localized PCa. METHODS A hierarchical, contingent survey was developed by consensus among a team of urologists, radiation oncologists, and medical oncologists. After piloting, it was electronically circulated to eligible members of the Canadian Urological Association, Genitourinary Radiation Oncologists of Canada, Urologist, Medical Oncologist and Radiation Oncologist Members of the American Medical Association, Urological Society of Australia and New Zealand and Confederacion Americana de Urologia. We characterized physicians' choices regarding PSA screening and PCa treatment. Among urologists and radiation oncologists, we assessed for correlation between specialty and treatment selection. RESULTS Of 893 respondents, 869 provided consent and completed the survey. Their median age was 50 years (IQR 41-60 years) and most were male (n=807; 93%) and lived in Canada (n=413; 47%) or the United States (n=143; 16%). 719 (83%) were urologists, 89 (10%) radiation oncologists, 9 (1%) medical oncologists, 8 (1%) other specialties (e.g. internist) and 45 did not provide specialty information. Of 807 male respondents, 494 (61%) had personally undergone PSA screening and 662 (82%) planned to in the future. Of 62 female respondents, 43 (69%) had recommended PSA testing to immediate family members. In total, 784 of 869 respondents (90%) endorsed past or future screening for themselves or for relatives. 30 (4%) of men had been diagnosed with PCa personally and 16 (26%) of women had recommended PCa treatment to an immediate family member. After restricting to responses from urologists and radiation oncologists, there was a significant correlation between physician specialty and the treatment selected (Phi coefficient=0.61; p=0.001). CONCLUSIONS Physicians who routinely treat PCa are very likely to undertake PCa screening themselves or recommend it for their immediate family members. Among those diagnosed with prostate cancer, there is a significant correlation between specialty and treatment selection. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e130 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Christopher Wallis More articles by this author Douglas Cheung More articles by this author Laurence Klotz More articles by this author Venu Chalasani More articles by this author Ricardo Leao More articles by this author Juan Garisto More articles by this author Gerard Morton More articles by this author Robert Nam More articles by this author Ian Tannock More articles by this author Raj Satkunasivam More articles by this author Expand All Advertisement 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.309 | 0.095 |
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".