Diagnostic delay in young women with breast cancer: A population-based analysis.
Bibliographic record
Abstract
92 Background: Breast cancer in young women is aggressive. Delays in diagnosis and treatment may contribute to the worse outcomes observed in the treatment of breast cancer in young women. The purpose of this study is to determine if there was diagnostic delay for women ≤ 35 with breast cancer in Alberta. Methods: We conducted a population based analysis of all women ≤ 35 treated for breast cancer between 2007 and 2010. Patient demographics, diagnostic modality, and time to treatment (surgery and radiation) were abstracted. “Diagnostic” and “treatment” delay were defined as ≥ 90 days from first imaging to pathologic diagnosis and pathologic diagnosis to surgery. Results: One hundred and one patient charts were identified in the Alberta Cancer Registry and reviewed. Mean age at diagnosis was 31.6 (± 3.3) years. Initial imaging modality was mammogram and ultrasound in 49.5% and 46.5% of women. The median time from imaging to pathologic diagnosis was 6 days (range 0-502). Of these, 11 (10.1%) women experienced diagnostic delay, with a median delay of 131 days (range 95-502). The stage at diagnosis was not significantly different for women with and without diagnostic delay (p = 0.47). For women with diagnostic delay, the initial imaging findings were most often reported as likely benign or recommended only follow up imaging. Mean time from diagnosis to surgery was 25 (± 14) days. Of patients undergoing surgery, 25 (24.8%) had breast conserving surgery. Radiation therapy occurred at a median of 48 (range 25-204) days post operatively or post chemotherapy. Conclusions: Ten percent of women ≤ 35 with breast cancer experienced a diagnostic delay. Diagnostic delay most often occurred due to initial imaging reports of benign or negative findings. Further studies are required to determine the impact of diagnostic delay on patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".