Engaging the radiology community in the National Clinical Trials Network: The ECOG-ACRIN TMIST experience.
Bibliographic record
Abstract
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 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".