Opportunities in Breast Cancer Patient Care in Southeast Ontario: Evidence-based Recommendations to Implement from Cancer Diagnosis to Treatment
Bibliographic record
Abstract
Despite advances in breast cancer biology and targeted treatments, surgery and early cancer detection are crucial for the best outcomes. In Ontario, the five-year breast cancer-specific mortality rates vary significantly, with Southeastern Ontario (SEO) being among the worst regions, especially in women aged 40-49. The purpose of this thesis was to determine feasible approaches to improve surgical outcomes and investigate potential clinicopathological determinants of poor survival rates in women 40-49. My first manuscript focused on assessing margin status at breast-conserving surgery (BCS) of women with breast cancer in 2016-2017 as inadequate margins (IM) often lead to reoperations and increase healthcare costs. Clinicopathological and imaging data were reviewed for all consecutive 360 patients. Statistical analysis revealed invasive cancers >20 mm, ductal carcinoma in situ, and, most importantly, the lack of a definitive presurgical diagnosis were associated with IM at BCS, which were 30.00%. My second manuscript focused on assessing clinicopathological, imaging, and genetic data collected for 395 patients aged 40-49 from 2009 to 2018. The hereditary cancer rate was within the previously reported range (8.10%), but metastatic rates were high (20.51%) regardless of genetic status and completion of intensive treatment. Moreover, my study showed that 88.35% of patients were symptomatic and only 4.56% had undergone annual screening. Cox analysis demonstrated that cancer dimension >20 mm was the strongest predictor of metastasis. Altogether, my research reveals the importance of a definitive presurgical diagnosis, which can be achieved with biopsy, and underscores the impact of early cancer detection through screening as cancer size is a major factor in both IM at BCS and high metastatic rates. Therefore, I propose practical, evidence-based approaches to improve breast cancer patient outcomes not only in SEO, but also in other regions that face similar challenges.
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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".