Wait Times and Diagnostic Pathways among Women with Invasive Breast Cancer in a Population-Based Publicly Funded Cancer Centre, British Columbia, Canada.
Bibliographic record
Abstract
Abstract Rationale and background: The Fraser Valley Cancer Center serves a large geographic population of urban and rural areas. Selected data on wait times and diagnostic pathways are available for mammography screening programs in British Columbia (BC), but not for patients presenting with clinical abnormalities such as a palpable breast lump. Diagnostic pathways in BC are managed by primary care physicians and community surgeons. Anecdotal evidence from oncology services suggests large variation in wait times and pathways.Objective: To examine time intervals and pathways between the detection of a breast abnormality and formal assessment by medical or radiation oncology for adjuvant treatment.Methods: Retrospective chart review of all patients with a new diagnosis of breast cancer first seen at the Fraser Valley Cancer Center in 2002, linked with demographic and other registry data. Data collected included type, location, date of investigations, surgical procedures, diagnosis, treatment and stage. Exclusion criteria were patients initially diagnosed with metastatic breast cancer, patients with recurrent breast cancer, male breast cancer patients, patients diagnosed or previously treated outside BC. Microsoft Access and SAS 9.2 were used to enter and analyse data.Results: From a total of 513 women diagnosed in 2002 with invasive breast cancer, 422 women (82%), without metastases (age 30 to 97 yrs, median 59 yrs), presented with abnormal screen (45%), breast lump (47%) or other findings. These women had up to 7 different radiological breast investigations and up to 5 surgical interventions. About 5% had 5 or more breast investigations. Median time (days) from presentation to first surgery was 41 days and varied by location of first investigation. Time to surgery was highest among women who presented with abnormal screening mammogram (median 49 days). Median time from first surgery consultation to first surgery was lowest for women presenting with breast lumps (15 days).Conclusions: These data indicate wait times beyond national and international targets and identified wide variability and some inefficient, redundant or unnecessary steps. Recognition and analysis of abnormal pathways will guide changes in breast health service delivery. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3083.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".