Improving Breast Cancer Diagnosis Pathways in Quebec
Bibliographic record
Abstract
Delays in breast cancer diagnosis can worsen the severity of illness and reinforce inequalities. This report analyzes Quebec’s capabilities and performance along the diagnosis pathway, gathering information from the scientific literature on cancer care, government reports, and expert interviews. The first section outlines which types of breast cancer data Quebec collects, and how data availability impacts the measurement of performance indicators. The second section discusses how socio-economic factors and unclear guidelines for patients outside Quebec’s organized screening program create barriers to diagnosis. We also explore how Quebec’s lack of standardized and integrated care and its outdated cancer registry can create further delays and inefficiencies. The final section of the report compares innovations in breast cancer diagnosis in Quebec to those in Alberta and Ontario, where diagnostic delays are shorter. This comparison suggests that Quebec should include high-risk individuals in its screening program, create personalized screening recommendations, update available imaging and genetic testing technologies, and modernize communication methods. Relevant research and initiatives seeking to increase screening adherence among groups with low screening rates are also discussed. Overall, this paper highlights tangible strategies to shorten and streamline the breast cancer diagnosis interval, and points the reader to key resources for further investigation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".