348 ASSOCIATION OF HEMATURIA ON MICROSCOPIC URINALYSIS AND RISK OF URINARY TRACT CANCER DEVELOPMENT
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Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».