Engaging the radiology community in the National Clinical Trials Network: The ECOG-ACRIN TMIST experience.
Notice bibliographique
Résumé
TPS10609 Background: ECOG-ACRIN launched the Tomosynthesis Mammographic Imaging Screening Trial (TMIST) through the National Cancer Institute’s National Clinical Trials Network (NCTN)— a network of academic medical centers, community hospitals, and private clinical practices that are committed to participating in NCI-funded clinical trials. The NCI NCTN was developed to support rapid trial start-up of NCI-funded cancer control/prevention, cancer treatment, and non-therapeutic clinical trials that occur within the institution through centralized institutional administration and shared clinical resource allocation (personnel, lab services). TMIST is a randomized clinical trial assessing two breast cancer screening imaging modalities, tomosynthesis and digital mammography, in the population of women presenting for screening mammography and therefore requires active involvement of radiology. Methods: TMIST seeks to enroll 164,946 women, ages 45 to 74 years who present for screening mammography. Because the population under evaluation are women already scheduled for screening mammography, the mammography clinic is critical to successful recruitment as well as adherence to imaging randomization assignments over a 5-year period and therefore must be actively engaged in this trial with a breast imaging radiologist championing the trial within this service. To get active engagement of breast imaging radiologists, we needed to first make them aware of TMIST. Breast imaging radiologists that were already actively involved in the NCTN received notification of the trial through the NCTN email lists. So our goal was to come up with a strategy to reach out to breast imaging radiologists that were not active members in the NCTN. This was achieved through in-person informational sessions to introduce the trial at national and international breast imaging meetings, introduction of the trial and the workings of the NCTN network to the radiology community through articles placed in American College of Radiology (ACR) newsletters, ads promoting TMIST on ACR social media platforms, and direct email by the TMIST study chair to key radiology stakeholders. As of February 15, 2021, there are 115 sites open: 106 in the U.S. and 9 internationally with an additional 54 sites planning to open. A total of 39,366 women are enrolled in the trial with two-thirds also consenting to optional blood and/or buccal cell collection. Minority populations’ participation in the trial is over 20%. A significant drop in enrollment occurred in Spring 2020 coinciding with the suspension of mammography services globally due to COVID-19 beginning mid-March 2020. Enrollment and follow-up screening visits for TMIST restarted in May 2020 and gradually ramped back up to pre-COVID totals in September 2020. Our highest monthly accrual so far occurred in November 2020 with 2,148 subjects enrolled. Clinical trial information: NCT03233191 .
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 | 0,114 | 0,068 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,007 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,005 |
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 ».