Meeting Abstracts from the British Society of Breast Radiology annual scientific meeting 2023
Bibliographic record
Abstract
1):O1The COVID-19 pandemic led to recommendations for decreased or ceased breast screening services.Prior studies that examine the COVID-19 impact on mammography volumes are limited to single regions and rates of less than 9 months post-pandemic.This study aims to assess the recovery of screening mammography volumes from 3 months pre COVID-onset through 12 months post COVID-onset across 7 participating sites from four countries.We collected mammography volume data from 7 breast screening services (Canada, Germany, USA, UK) between December 2019 and April 2021 using an artificial intelligence software tool.The study was approved by the research ethics at participating sites.249,817 screening mammograms were collected.Of the 7 participating sites, 4 returned to and occasionally exceeded pre-COVID volumes, whereas the other 3 sites approached but did not fully return to pre-COVID volumes in the 12 months post COVID.Sites varied substantially in the time taken to re-initiate screening mammography after the first COVID wave (1-6 months).One site shut down screening services in successive COVID waves, while other sites experienced short dips in volumes but generally recovered within one month.In the 1-year period post COVID-onset, mammography screening volumes recovered to varying extents at differing rates, with some sites returning to pre-pandemic levels and others lagging behind.This international multi-center study may inform future opportunities for collaboration between sites to develop strategies for increasing screening volumes and sharing best practices for pandemic recovery. O2.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.477 | 0.385 |
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".