Abstract P4-03-09: Time points to diagnosis and treatment of invasive breast cancer in southwestern Ontario
Bibliographic record
Abstract
Abstract Background Delays in breast cancer diagnosis and treatment are associated with increased tumour size at presentation, higher incidence of lymph node metastasis, higher relapse rates and lower 5-year survival rates in some studies. Diagnosis and treatment of breast cancer is complex, involving many health care practitioners at multiple locations. New advances in diagnostic and prognostic indicators make the process ever more complex. We reviewed the timing of crucial events in breast cancer diagnosis and treatment in Southwestern Ontario. Methods: Two time periods (2001, 2011) were assessed. Patients with new early invasive breast cancer with specific TNM criteria (T1c or greater plus any N, or any T plus N1-3; M0) were included. Data collection and analysis explored significant time points from first suspicion of malignancy, to diagnosis and definitive treatment, as well as patient demographic information. Results: 300 and 451 eligible patients were identified in 2001 and 2011 cohorts. Distribution of T and N status by cohort was compared by Chi-square test and time from first suspicion to diagnosis by Wilcoxon testing. The proportion of patients in which the time from first suspicion (clinical by lay person or health professional or by imaging) to pathological diagnosis exceeded two months increased from 20.4% in 2001 to 42.1% in 2011. There were no differences in time from first suspicion to diagnosis when analyzed by age (p = 0.54) or location (p = 0.50). Pathological T2-4 status at diagnosis increased from 48.4% in 2001 to 56.8% in 2011, and N2-3 status increased from 7.3% to 12.6%. Patients who had mammograms increased from 52.7% to 59.4%. A positive or suspicious mammogram was the first sign in 36% and 39.1% of cases. There was a trend towards more pathological diagnoses and definitive surgeries at tertiary centers compared to community hospitals. Conclusions: There is a longer time interval from first suspicion of malignancy to pathological diagnosis in 2011 compared to 2001 for both urban and rural populations. The number of non-low risk, non-metastatic cancers at diagnosis increased by 50% over the time interval studied (300 v 451), but number of T2-T4 non-metastatic cancers increased by 77% (145 v 256) and N2,3 cancers by 136% (22 v 52). This is despite more patients receiving mammograms (52.7% v 59.4%). Delays in breast cancer treatment are multifactorial, including both system-, and patient-related factors. This is an under-researched area and more investigation is needed to understand the reasons for the diagnostic delays and more serious cancers at presentation in order to improve outcomes. Reasons for higher proportions of advanced cancers will be discussed. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-03-09.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".