ECOG-ACRIN tomosynthesis mammographic imaging screening trial (EA1151).
Bibliographic record
Abstract
TPS1597 Background: This randomized trial is intended to determine whether tomosynthesis (TM) should replace the current standard for breast cancer (BC) screening, digital mammography (DM). It is hypothesized that the population of women assigned TM screening for 3-5 rounds will have fewer advanced cancers than the population assigned to DM screening. Methods: 164,946 women, ages 45 to 74 years who present for screening mammography and consent to participate will be enrolled across 150 sites in the US, Canada and abroad. Women will be randomized to TM or DM. The frequency and number of screening examinations over a five year period will vary based on menopausal status and whether they have specific risk factors, including - hormone use, family history of BC, deleterious genes, prior benign breast biopsy with diagnosis of LCIS or atypia any kind, or dense breasts. Blood and buccal cells will be collected from as many enrolled women as are willing to provide the samples. All breast biopsies during the trial will undergo gene expression analysis for the PAM50 and other progression pathways (PAM50-plus). All subjects enrolled will be followed long term for at least eight years. The primary endpoint is the proportion of participants who have an advanced breast cancer diagnosed at any time within 4.5 years of randomization in to the trial. Secondary endpoints include measures of diagnostic and predictive performance; rates of recall, biopsy, and interval cancers, prevalence of breast cancer subtypes, and tumor subtype based on PAM50-plus analysis. As of January 17th 2020, there are 104 sites open and 21,452 women enrolled in the trial. The DSMC last reviewed the trial in June 2019 and suggested that the trial continue as planned. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".