MP33-06 EXPERIENCE OF THE VIRTUAL CANADIAN TESTICULAR CANCER SECOND OPINION GROUP
Notice bibliographique
Résumé
You have accessJournal of UrologyCME1 Apr 2023MP33-06 EXPERIENCE OF THE VIRTUAL CANADIAN TESTICULAR CANCER SECOND OPINION GROUP J Jesus Cendejas-Gomez, Robert James Hamilton, Michel Jewett, Victor Sandoval, and Nicholas Edgar Power J Jesus Cendejas-GomezJ Jesus Cendejas-Gomez More articles by this author , Robert James HamiltonRobert James Hamilton More articles by this author , Michel JewettMichel Jewett More articles by this author , Victor SandovalVictor Sandoval More articles by this author , and Nicholas Edgar PowerNicholas Edgar Power More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003266.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Testicular cancer (TC) is the most frequent solid neoplasia in male patients between 15-35 years. The cases complexity increases in the metastatic stages and the implementation of evidence into the practice is more difficult. Second-opinion groups have shown their utility in improving the implementation of evidence-based treatments. Our main objective is to analyze the utility of a second opinion group in changing decision-making in patients with TC in Canada. As secondary objectives, we will analyze the number of answers per question, the average waiting time for second opinions, the different types of questions, and the over and undertreatment. METHODS: This is a retrospective analysis of 132 cases from the virtual Canadian testicular cancer second opinion group. The discussion of cases was carried out anonymously on the Google groups platform, in which doctors from 9 different provinces and different centers throughout Canada participate, including oncologists, urologist oncologists, and radiotherapists. Collected data include patients' demographic, histological, and treatment data. From the medical standpoint the number of answers, type of question, measured concordance between first and second opinions, and finally change in treatments after the discussion. The information was analyzed from June 2014 to July 2022. RESULTS: We included 132 cases of patients with testicular tumors and extragonadal primary germ cell tumors (GCT) in Canada.The most common histology was GCT in 94% (124/132), non-seminomas represented 72.7% (96/ 124). The most common clinical stage was metastatic in 94.7%.The mean of second opinions was 4.7, the 81% of seekers got responses from at least 3 different centers, and 56.8% of questions received a response from at least two different specialties. The average waiting time for the total of second opinions was less than 1 day. The questions come from academic centers in 81%, and the most common seeker doctors were the oncologists with 86.4% of the questions. The most frequent kind of question was related to chemotherapy in 49%.Comparing the first and second opinions, we found 24% of overtreatment and 26% of undertreatment, with a surprising 52% of potentially changing decision-making. In the multivariable analysis we did not find statistically significant predictors for changing decision-making. CONCLUSIONS: A virtual second opinion group could be a very valuable and easily accessible tool to improve the treatment of patients with complex testicular cancers, with an important percentage of potential changes in treatment even in academic cancer centers. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e452 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information J Jesus Cendejas-Gomez More articles by this author Robert James Hamilton More articles by this author Michel Jewett More articles by this author Victor Sandoval More articles by this author Nicholas Edgar Power More articles by this author Expand All 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 | 0,002 | 0,005 |
| 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 | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,175 | 0,019 |
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 ».