Abstract P3-02-02: Impact of breast density notification laws on radiology practices: A survey of 110 radiology facilities
Notice bibliographique
Résumé
Abstract Purpose: Breast Density Notification laws, passed in 15 states as of April 2014, mandate that breast density information be given to patients often without guidance on modalities, patient selection or funding for supplemental screening. The purpose of this study is to assess the impact of breast density notification laws on radiology practices, specifically regarding breast cancer risk assessment and supplemental screening studies. Methods: We performed an anonymous 20-question web-based survey to Society of Breast Imaging radiologists using a Qualtrics Survey Tool between 8/2013-3/2014, with questions on radiology practices, breast cancer risk assessment, breast density measurement, supplemental screening tests, and support for referring physicians and patients. We compared survey results between groups using Fisher’s exact test. Results: 121 radiologists from 110 facilities (48 academic, 43 large private hospital, 15 small private hospital and 4 other) representing 34 USA states and 1 Canadian site responded. 49% of facilities (54/110) were in states with an enacted breast density notification law. 37% of facilities (40/109) performed risk assessment, 26% (28/109) did not perform risk assessment, and 38% (41/109) did not but reported family history/other risk factors, with no significant difference in performing risk assessment between facilities with or without an enacted law (p-value 0.71). Of the 37 facilities performing risk assessment, 60% used the Gail model, 22% used the Tyrer-Cuzick model and 11% used the modified Gail model (multiple answers allowed [m.a.a.]). Of the 15 facilities performing risk assessment, 40% answered "yes" when asked whether performing risk assessment is a new task because of the density law. Breast density was estimated by only visual assessment in 98% of facilities (103/105), and by computer-based determination with or without visual assessment in 2% (2/105). Supplemental screening studies offered included magnetic resonance imaging (MRI) (88%, 92/105), handheld whole breast ultrasound (HHWBUS) (48%, 50/105), tomosynthesis (39%, 41/105), and automated WBUS (8%, 8/105) (m.a.a.). There was no significant difference in supplemental screening studies offered between facilities with or without an enacted law (p-value 0.26). In anticipation of the law, facilities implemented HHWBUS (33%, 16/48), tomosynthesis (6%, 3/48), automated WBUS (6%, 3/48) or none (60%, 29/48) (m.a.a.). Facilities with the enacted law prepared for the law with referring physician discussions (69%, 34/49), website (49%, 24/49), educational talks for referring physicians (43%, 21/49) or patients (31%, 15/49) (m.a.a.). Conclusion: Our survey showed variations in available supplemental screening modalities and policy implementation at each facility. There was no significant difference in performing risk assessment and supplemental screening studies between facilities with or without an enacted breast density notification law. Citation Format: Lina Nayak, Kanae K Miyake, Yueyi Irene Liu, William R Thomas, Edward A Sickles, Bonnie N Joe, Karen Lindfors, R J Brenner, Stephen Feig, Lawrence W Bassett, Jessica W Leung, Haydee Ojeda-Fournier, Jonathan Hargreaves, Elissa Price, Jafi A Lipson, Allison W Kurian, Elyse Love, Donna D Walgenbach, Lauren Ryan, Meg Durbin, Bruce L Daniel, Linda Garcia, Debra M Ikeda. Impact of breast density notification laws on radiology practices: A survey of 110 radiology facilities [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-02-02.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».