Bibliographic record
Abstract
Breast cancer remains the most common type of cancer among Canadian women, with 28,900 new cases in 2022 alone. Improved detection through screening mammography and advances in multi-modality therapy account for the decline in breast cancer mortality seen in Canada since the 1980s. As 5-year survival rates reach 89%, the number of breast cancer survivors is rising. The concept of cancer survivorship has existed for decades, as has the appreciation that it is a complex domain of cancer care that begins at the time of diagnosis. Even within the group of patients with breast cancer, survivorship experiences and care needs are diverse, reflecting variability in tumour clinicopathologic characteristics, treatment plans, and prognosis. Evidence-based tools and guidelines suggest the assessment and management of cancer survivor’s physical, psychological, social, financial, and employment well-being. There is a need to clinically monitor for breast cancer recurrence and the development of secondary malignancies through screening. Survivorship also warrants attention to health promotion, including weight management, nutrition, physical activity, preventive health, and cessation of alcohol and cigarettes. The provision of survivorship care is the responsibility of all healthcare professionals, which requires close coordination between primary care and specialized cancer centres. In this article, we focus on the physical and psychosocial long-term and late effects faced by survivors of early-stage breast cancer. Many adjuvant therapies for breast cancer are associated with toxicities that negatively impact quality of life (QoL) and adherence. Nonadherence is important to address because it compromises breast cancer outcomes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".