PD19-11 WHAT FALSE-NEGATIVE RATES ARE BLADDER CANCER PATIENTS AND URO-ONCOLOGISTS WILLING TO ACCEPT IN ORDER TO AVOID SURVEILLANCE CYSTOSCOPY?
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Résumé
You have accessJournal of UrologyBladder Cancer: Non-invasive II1 Apr 2017PD19-11 WHAT FALSE-NEGATIVE RATES ARE BLADDER CANCER PATIENTS AND URO-ONCOLOGISTS WILLING TO ACCEPT IN ORDER TO AVOID SURVEILLANCE CYSTOSCOPY? Rashid Sayyid, Abdallah Sayyid, Ricardo Leao, Ardalanejaz Ahmad, Hanan Goldberg, Robert Hamilton, Girish Kulkarni, Antonio Finelli, Alexandre Zlotta, and Neil Fleshner Rashid SayyidRashid Sayyid More articles by this author , Abdallah SayyidAbdallah Sayyid More articles by this author , Ricardo LeaoRicardo Leao More articles by this author , Ardalanejaz AhmadArdalanejaz Ahmad More articles by this author , Hanan GoldbergHanan Goldberg More articles by this author , Robert HamiltonRobert Hamilton More articles by this author , Girish KulkarniGirish Kulkarni More articles by this author , Antonio FinelliAntonio Finelli More articles by this author , Alexandre ZlottaAlexandre Zlotta More articles by this author , and Neil FleshnerNeil Fleshner More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.884AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Surveillance cystoscopy in patients with non-muscle invasive bladder cancer is associated with pain, anxiety, and often necessitates antibiotic prophylaxis. Novel imaging and blood/urine based non-invasive alternatives are being developed to detect bladder cancer recurrence/progression in this patient population. We conducted a questionnaire-based hypothetical study to determine what test performance characteristics and cost would a non-invasive test(s) need in order for patients and their physicians to avoid cystoscopy. METHODS A questionnaire was administered to two populations (patients with previous history of non-muscle invasive bladder cancer and uro-oncologists) to establish an acceptable false negative (FN) rate and cost for such test(s). Patients were surveyed at time of follow up in the cystoscopy clinic at Toronto General Hospital. Physician members of the Society of Urologic Oncology were surveyed via an online questionnaire. Participants were questioned regarding demographics and other characteristics that might influence chosen error rate and cost. A chi-square test was used to determine if such relationships exist. Statistical significance was set at p <0.05. RESULTS 137 patient and 51 physician responses were obtained. 102 (75%) of the patients were male and 35 (25%) were female. 77% of patients were not comfortable with a non-invasive test(s) in place of repeat cystoscopy, with a further 14% requesting a false-negative (FN) rate of 0.5% or better. 75% of uro-oncologists were comfortable with an alternative non-invasive test, with 31% of responders requesting a FN rate of 5% or better and 33% a FN rate of 1% or better. A cost of $100-500 was deemed appropriate by 61% of physician responders. Demographics and other participant characteristics did not influence FN rate or cost choices. CONCLUSIONS Majority of bladder cancer patients are not comfortable with a non-invasive test(s) in place of surveillance cystoscopy, as opposed to most uro-oncologists who are. Given the importance of patient input in clinical decision-making, it appears that non-invasive tests will not replace surveillance cystoscopies in the near future, unless they achieve equivalent accuracy. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e369 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Rashid Sayyid More articles by this author Abdallah Sayyid More articles by this author Ricardo Leao More articles by this author Ardalanejaz Ahmad More articles by this author Hanan Goldberg More articles by this author Robert Hamilton More articles by this author Girish Kulkarni More articles by this author Antonio Finelli More articles by this author Alexandre Zlotta More articles by this author Neil Fleshner 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 | 0,006 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,007 |
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 ».