Marrying Story with Science: The Impact of Outdated and Inconsistent Breast Cancer Screening Practices in Canada
Bibliographic record
Abstract
Behind the science of breast cancer in Canada, as well as globally, are the stories of thousands of women, their families, and their communities. These include stories from those who have died or those suffering from the realities of stage III and stage IV breast cancer due to late detection, misinformation, and dismissal. The reality for these women is that, whilst grateful for the latest developments in cancer research, much of this knowledge is not reflected in policy and practice. Canadian guidelines do not reflect the recommended screening by experts within the field and inequities in screening practices and practitioner knowledge exist in different areas within Canada. Told through the stories of women with lived experiences of late-stage breast cancer and supported by scientific evidence, this paper explores the impact of outdated breast cancer screening practices on the lives of women. Recent patient advocacy is driving changes, such as notifying women of their breast density in a few jurisdictions in Canada, but we call for the whole medical community to take responsibility and ensure breast screening is optimised to save more lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.038 | 0.025 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".