1638 SHOULD VOIDING SYMPTOMS BASED ON AUA SYMPTOM SCORE BE USED TO ORDER A PSA TEST?
Bibliographic record
Abstract
You have accessJournal of UrologyBenign Prostatic Hyperplasia: Epidemiology and Natural History/Evaluation and Markers1 Apr 20101638 SHOULD VOIDING SYMPTOMS BASED ON AUA SYMPTOM SCORE BE USED TO ORDER A PSA TEST? Dan Lewinshtein, Jason Kim, Stephen Lukasewycz, and Christopher Porter Dan LewinshteinDan Lewinshtein More articles by this author , Jason KimJason Kim More articles by this author , Stephen LukasewyczStephen Lukasewycz More articles by this author , and Christopher PorterChristopher Porter More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2010.02.1419AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The AUA and the Canadian Urological Association (CUA) both suggest asking for a PSA in patients with lower urinary tract symptoms (LUTS) and a life expectancy more than ten years. We explored whether the AUA symptom score (AUASS) could predict PSA level and prostate needle biopsy (PNB) outcomes. METHODS We reviewed an IRB approved prospective database of patients referred to our clinic for elevated PSA or a suspicious digital rectal examination. The patients had all completed an AUASS questionnaire. AUASS was modeled as a categorical variable based on severity. We used logistic and linear regression and receiver operator curve (ROC) analysis to evaluate the ability of AUASS to predict PSA and PNB outcome. We adjusted for age and prostate volume (PV). The Kruskal-Wallis test was used to detect a difference in median PSA between the AUASS categories. RESULTS The cohort consisted of 890 patients. Median age, prostate volume(PV), and AUASS were 63 years, 35cc, and 8 points, respectively. 48%, 44% and 8% were AUASS mild, moderate, and severe, respectively. Median PSA was 4.5 ng/ml. The Kruskal-Wallis test revealed no difference in median PSA amongst the three AUASS categories. On univariate analysis, AUASS was not a significant predictor (p>0.05) of biopsy outcome or PSA, modeled as a continuous variable. However, AUASS was predictive (OR=1.6 & 1.3, p<0.001) when PSA was modeled as a binary outcome (>4.0 or not). On multivariate analysis, when adjusted for age and prostate volume, AUASS was no longer predictive (p=0.1) of PSA level. With ROC analysis, the predictive accuracy of AUASS for PSA (see figure) and biopsy outcome was 55% and 49%, respectively. CONCLUSIONS In our analysis, AUASS did not predict PSA level. Therefore, patients in our cohort with abnormal voiding based on AUASS, are not at increased risk of having an elevated PSA. Seattle, WA© 2010 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 183Issue 4SApril 2010Page: e633 Advertisement Copyright & Permissions© 2010 by American Urological Association Education and Research, Inc.MetricsAuthor Information Dan Lewinshtein More articles by this author Jason Kim More articles by this author Stephen Lukasewycz More articles by this author Christopher Porter 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".