Adherence to guidance for prioritizing higher risk groups for breast cancer screening during the COVID-19 pandemic in the Ontario Breast Screening Program: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Breast cancer screening in Ontario, Canada, was deferred during the first wave of the COVID-19 pandemic, and a prioritization framework to resume services according to breast cancer risk was developed. The purpose of this study was to assess the impact of the pandemic within the Ontario Breast Screening Program (OBSP) by comparing total volumes of screening mammographic examinations and volumes of screening mammographic examinations with abnormal results before and during the pandemic, and to assess backlogs on the basis of adherence to the prioritization framework. METHODS: A descriptive study was conducted among women aged 50 to 74 years at average risk and women aged 30 to 69 years at high risk, who participated in the OBSP. Percentage change was calculated by comparing observed monthly volumes of mammographic examinations from March 2020 to March 2021 with 2019 volumes and proportions by risk group. We plotted estimates of backlog volumes of mammographic examinations by risk group, comparing pandemic with prepandemic screening practices. Volumes of mammographic examinations with abnormal results were plotted by risk group. RESULTS: Volumes of mammographic examinations in the OBSP showed the largest declines in April and May 2020 (> 99% decrease) and returned to prepandemic levels as of March 2021, with an accumulated backlog of 340 876 examinations. As of March 2021, prioritization had reduced the backlog volumes of screens for participants at high risk for breast cancer by 96.5% (186 v. 5469 expected) and annual rescreens for participants at average risk for breast cancer by 13.5% (62 432 v. 72 202 expected); there was a minimal decline for initial screens. Conversely, the backlog increased by 7.6% for biennial rescreens (221 674 v. 206 079 expected). More than half (59.4%) of mammographic examinations with abnormal results were for participants in the higher risk groups. INTERPRETATION: Prioritizing screening for those at higher risk for breast cancer may increase diagnostic yield and redirect resources to minimize potential long-term harms caused by the pandemic. This further supports the clinical utility of risk-stratified cancer screening.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".