503 AN INTERNATIONAL MULTI-CENTRE STUDY EXAMINING DIAGNOSTIC CRITERIA FOR ACTIVE SURVEILLANCE IN MEN UNDERGOING RADICAL PROSTATECTOMY
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Localized II1 Apr 2012503 AN INTERNATIONAL MULTI-CENTRE STUDY EXAMINING DIAGNOSTIC CRITERIA FOR ACTIVE SURVEILLANCE IN MEN UNDERGOING RADICAL PROSTATECTOMY Lih-Ming Wong, David Neal, Richard Johnston, Nimesh Shah, Anne Warren, Chris Hoven, Larry Goldenberg, Martin Gleave, Anthony Costello, and Niall Corcoran Lih-Ming WongLih-Ming Wong Cambridge, United Kingdom More articles by this author , David NealDavid Neal Cambridge, United Kingdom More articles by this author , Richard JohnstonRichard Johnston Cambridge, United Kingdom More articles by this author , Nimesh ShahNimesh Shah Cambridge, United Kingdom More articles by this author , Anne WarrenAnne Warren Cambridge, United Kingdom More articles by this author , Chris HovenChris Hoven Melbourne, Australia More articles by this author , Larry GoldenbergLarry Goldenberg Vancouver, Canada More articles by this author , Martin GleaveMartin Gleave Vancouver, Canada More articles by this author , Anthony CostelloAnthony Costello Melbourne, Australia More articles by this author , and Niall CorcoranNiall Corcoran Melbourne, Australia More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.574AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES There are numerous inclusion criteria for active surveillance (AS) and discrepancies reflect the uncertainty in predicting the true rate of prostate cancer with adverse features. Given that disease characteristics may display regional differences, it is possible that the application of a selection rule generated from one population may result in inaccurate estimation of disease risk when applied to different cohorts. We examined effects of Klotz and Van den Bergh (Prostate Cancer Research International: Active Surveillance, PRIAS) AS selection criteria on the detection of true low risk prostate cancer. METHODS From three centers in UK, Canada and Australia, prospective data on men who underwent radical prostatectomy was collated. Men initially suitable for AS, according to Klotz and PRIAS criteria, had prostatectomy specimens analyzed for pathological upgrading (Gleason score >7) and upstaging (>pT3 disease). Mann-Whitney U or Kruskal-Wallis ANOVA modelling evaluated differences in continuous variables and Pearson's Chi-squared or Fisher's exact test determined differences between categorical variables. Multivariable logistic regression was performed to identify predictors of high-risk disease. A nomogram was generated by logistic regression analysis, and performance characterized by ROC curves. RESULTS 800 men met the Klotz criteria, and 410 met the more stringent PRIAS criteria. For Klotz and PRIAS groups, the rates for upgrading were 50.6%, and 42.7%, and upstaging 17.6%, 12.4% respectively. Significant predictors of high-risk disease were: - Klotz group: increasing age (OR1.04, p=0.02), presence of palpable disease (OR1.54, p=0.045), centre of diagnosis (Cambridge OR2.85, p<0.001) and number of positive cores (OR1.25, p<0.001). - PRIAS group: increasing PSA (OR1.15, p=0.043) and presence of palpable disease (OR1.85, p=0.04). Cambridge had a high pT3a rate (26% vs. 12%). To assist selection of men in the UK for AS, from the Cambridge data, we generated a nomogram predicting high-risk features in patients who meet the Klotz criteria (AUC of 0.72). CONCLUSIONS The rate of high-risk disease in prostatectomy patients who pre-operatively meet criteria for AS varies geographically. We found higher rates of re-classification (42.7-50.6%) than previously reported from Europe and North America (23-35%). With more stringent selection criteria, there is less reclassification but also fewer men who may benefit from AS. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e206-e207 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lih-Ming Wong Cambridge, United Kingdom More articles by this author David Neal Cambridge, United Kingdom More articles by this author Richard Johnston Cambridge, United Kingdom More articles by this author Nimesh Shah Cambridge, United Kingdom More articles by this author Anne Warren Cambridge, United Kingdom More articles by this author Chris Hoven Melbourne, Australia More articles by this author Larry Goldenberg Vancouver, Canada More articles by this author Martin Gleave Vancouver, Canada More articles by this author Anthony Costello Melbourne, Australia More articles by this author Niall Corcoran Melbourne, Australia More articles by this author Expand All Advertisement Advertisement PDF DownloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".