Diagnostic delay among young women with breast cancer.
Bibliographic record
Abstract
117 Background: Breast cancer in women under 40 is uncommon, but often presents with greater disease burden and worse survival compared to the post-menopausal population. Given its infrequence, it is not well-understood how young patients initiate contact with care and how they are investigated prior to establishing the diagnosis. Whether young women experience diagnostic delay and whether it might impact survival is unclear. We aimed to describe the presentation of breast cancer among women <40 and to understand the diagnostic timeline using patient-reported outcomes. Methods: We evaluated patients in the Reducing the Burden of Breast Cancer in Young Women (RUBY) cohort, a pan-Canadian study recruiting women <40 with breast cancer at the time of diagnosis. Five online surveys detailing demographics, medical/family history and the diagnostic journey, distributed by e-mail at the time of recruitment, were used. All subjects who completed the surveys from 2015-2022 were included. A “patient delay” was considered >4 weeks from onset of symptoms to first contact with the healthcare system. A “system delay” was considered >3 weeks from presentation to first investigation, based on Canadian Partnership Against Cancer screening guidelines. Analysis was conducted using descriptive statistics. Results: There were 1148 patients included for analysis. Median age (IQR) was 37 (33.9, 39.0). Eighty-nine percent of patients had a symptom prompting assessment, of which most had a palpable mass (77.3%), and 27.7% had a painful mass. The median (IQR) number of weeks that patients waited was 2.0 (1.0, 8.0), and 364 patients (31.7%) experienced a patient delay. Most patient delays were attributed to lack of awareness of associated symptoms: 27.7% of patients were not concerned about their symptom and 3.9% were falsely reassured by practitioners. Other reasons included difficulty accessing timely care (3.6%; 37 patients) and conflicting priorities (2.1%; 21 patients). One hundred sixteen patients (10.1%) experienced a system delay. When asked about perceived timeliness of diagnosis, only 248 patients (24.4%) felt their time to assessment was not fast enough. Conclusions: The majority of young women with breast cancer present with palpable, often painful masses. A significant proportion of women experienced a patient delay, suggesting opportunities for increased education regarding presentation of early-onset breast cancer; After presentation to care, most women were investigated in a timely manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".