348 ASSOCIATION OF HEMATURIA ON MICROSCOPIC URINALYSIS AND RISK OF URINARY TRACT CANCER DEVELOPMENT
Bibliographic record
Abstract
You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Practice Patterns, Cost Effectiveness I1 Apr 2010348 ASSOCIATION OF HEMATURIA ON MICROSCOPIC URINALYSIS AND RISK OF URINARY TRACT CANCER DEVELOPMENT Howard Jung, Joseph Gleason, Jeff Slezak, Ronald Loo, Hetal Patel, Gary Chien, and Steven Jacobsen Howard JungHoward Jung More articles by this author , Joseph GleasonJoseph Gleason More articles by this author , Jeff SlezakJeff Slezak More articles by this author , Ronald LooRonald Loo More articles by this author , Hetal PatelHetal Patel More articles by this author , Gary ChienGary Chien More articles by this author , and Steven JacobsenSteven Jacobsen More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2010.02.414AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Detection of urinary tract cancer is of paramount importance during evaluation of hematuria. The American Urological Association (AUA) recommends evaluation of a patient with microscopic hematuria defined as 3 or more RBC/HPF from at least 2 urinalysis specimens. Meanwhile, the Canadian Urological Association (CUA) recommends evaluation within these parameters only if the patient is more than 40 years old. Currently, no large population study is available to validate either recommendation. The purpose of this study is to determine the incidence of urinary tract cancer in patients with hematuria, to stratify risk according to age, sex, and degree of hematuria, and to examine current best policy recommendations. METHODS This is a retrospective cohort study including all members in a large health maintenance organization with hematuria diagnosed by microscopic urinalysis from January 1, 2004 to December 31, 2005. Members with recent hospitalization, pregnancy, urinary tract infection, or prior cancer diagnosis were excluded. The primary outcome was the diagnosis of malignancy associated with the upper or lower urinary tracts by the end of 2008. Further analysis according to age, gender, and degree of hematuria was performed. Logistic regression was used to model the probability of cancer detection. RESULTS The cohort includes 309,402 members with at least one urinalysis in the defined time period. Of them, 156,691 demonstrated hematuria. There were 1,353 urinary tract cancers identified in the cohort at the end of 3 years. Of them, 1,071 demonstrated hematuria on at least one urinalysis. Urinary tract cancer rates were associated with older age (OR for >40 =17.0, 95% CI=11.2-25.7), degree of hematuria (OR for >25 RBC/HPF =4.0, CI=3.5-4.5), and male sex (OR =4.8 CI=4.2-5.6). Using the AUA recommendations, we calculated a sensitivity of 50.2%, specificity of 83.8%, and positive predictive value (PPV) of 1.3%. Using the CUA recommendations, we calculated a sensitivity of 49.2%, specificity of 88.4%, and PPV of 1.8%. Using an alternative cutoff of >25RBC/HPF in members >40, we calculated a sensitivity of 50.4%, specificity of 92.2%, and PPV of 2.8%. CONCLUSIONS Current recommendations for the evaluation of hematuria yield low rates of cancer detection. Meanwhile, certain populations, such as young age groups with low degrees of hematuria, may safely be spared full evaluation. These findings suggest the need for an alternative policy to improve identification of patients at risk of developing urinary tract cancer. Los Angeles, CA© 2010 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 183Issue 4SApril 2010Page: e138 Advertisement Copyright & Permissions© 2010 by American Urological Association Education and Research, Inc.MetricsAuthor Information Howard Jung More articles by this author Joseph Gleason More articles by this author Jeff Slezak More articles by this author Ronald Loo More articles by this author Hetal Patel More articles by this author Gary Chien More articles by this author Steven Jacobsen 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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.002 |
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".