PD44-11 UNDERSTANDING THE PERFORMANCE OF ACTIVE SURVEILLANCE SELECTION CRITERIA IN REAL-WORLD PRACTICE
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Detection and Screening IV1 Apr 2015PD44-11 UNDERSTANDING THE PERFORMANCE OF ACTIVE SURVEILLANCE SELECTION CRITERIA IN REAL-WORLD PRACTICE Scott Hawken, Paul Womble, Lindsey Herrel, Zaojun Ye, Susan Linsell, James Montie, and David Miller Scott HawkenScott Hawken More articles by this author , Paul WomblePaul Womble More articles by this author , Lindsey HerrelLindsey Herrel More articles by this author , Zaojun YeZaojun Ye More articles by this author , Susan LinsellSusan Linsell More articles by this author , James MontieJames Montie More articles by this author , and David MillerDavid Miller More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2557AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Although there are many published guidelines for selecting patients for Active Surveillance (AS), little is known about how well these work in real-world practice. We used data from the Michigan Urological Surgery Improvement Collaborative (MUSIC) to evaluate the performance of several published guidelines for identifying patients actually undergoing initial AS in diverse community and academic practices. METHODS From March 2012 through October 2014, clinicopathologic and treatment data for 4,934 men with newly-diagnosed prostate cancer were entered into the MUSIC registry. For several accepted AS guidelines (Table), we calculated the proportion of all men meeting each set of selection criteria that actually entered AS (defined as the sensitivity of the guideline for real world practice patterns). Using the single guideline determined to be most sensitive for the entire cohort, we then compared demographics, tumor volume, and life expectancy (based on a published algorithm) for patients meeting this guideline who entered AS and those who received definitive therapy. RESULTS Overall, 871 men (20%) underwent initial AS. When applied to the entire patient cohort, published guidelines varied widely in their sensitivity for identifying patients initiating AS, ranging from 49% (Toronto) to 64% (Johns Hopkins, JH) (Table). At a practice-level, the sensitivity of the JH guideline (the most sensitive for the entire cohort) spanned from 29% to 84% across participating sites (p<0.001). Compared with men undergoing initial AS, patients meeting the JH criteria that received definitive therapy were more likely to have 2 (vs 1) positive cores on biopsy (p=0.003). The greatest percentage of a core positive for cancer was also higher in treated men (13% vs 11%, p=0.01). The proportion of patients with life expectancy greater than 10 years was similar for these two groups (p=0.4). CONCLUSIONS Published AS selection guidelines vary widely in their sensitivity for identifying men who initiate this treatment in real-world practice. Among patients meeting the most sensitive criteria, those who received definitive therapy had evidence of higher tumor volume, but not longer life expectancy, underscoring the influence of even small differences in cancer severity on treatment decisions. Active Surveillance selection criteria and performance Guideline Gleason Score PSA PSA Density T-Stage Positive Cores GPC* (%) Patients Meeting Selection Criteria (n) Sensitivity (95% CI) JH ≤ 6 – < 0.15 cT1c ≤ 2 ≤ 50 463 64 (59-68) NCCN: Very Low Risk ≤ 6 <10 < 0.15 cT1c ≤ 2 ≤ 50 445 63 (59-68) MSKCC ≤ 6 <10 – ≤ cT2a ≤ 3 < 50 948 56 (53-60) UCSF ≤ 6 ≤10 – ≤ cT2 ≤ 33% ≤ 50 1048 54 (51-57) NCCN: Low Risk ≤ 6 <10 – ≤ cT2a – – 1228 50 (47-52) Toronto ≤ 6 <10 – – – – 1282 49 (46-51) *GPC: Greatest Percentage Positive of a Biopsy Core © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e901 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Scott Hawken More articles by this author Paul Womble More articles by this author Lindsey Herrel More articles by this author Zaojun Ye More articles by this author Susan Linsell More articles by this author James Montie More articles by this author David Miller 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 | 0.049 | 0.283 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".