Factors related to breast cancer detection mode and time to diagnosis in Alberta, Canada: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Understanding the factors affecting the mode and timeliness of breast cancer diagnosis is important to optimizing patient experiences and outcomes. The purposes of the study were to identify factors related to the length of the diagnostic interval and assess how they vary by mode of diagnosis: screen or symptom detection. METHODS: All female residents of Alberta diagnosed with first primary breast cancer in years 2004-2010 were identified from the Alberta Cancer Registry. Data were linked to Physician Claims and screening program databases. Screen-detected patients were identified as having a screening mammogram within 6-months prior to diagnosis; remaining patients were considered symptom-detected. Separate quantile regression was conducted for each detection mode to assess the relationship between demographic/clinical and healthcare factors. RESULTS: Overall, 38 % of the 12,373 breast cancer cases were screen-detected compared to 47 % of the screen-eligible population. Health region of residence was strongly associated with cancer detection mode. The median diagnostic interval for screen and symptom-detected cancers was 19 and 21 days, respectively. The variation by health region, however, was large ranging from an estimated median of 4 to 37 days for screen-detected patients and from 17 to 33 days for symptom-detected patients. Cancer stage was inversely associated with the diagnostic interval for symptom-detected cancers, but not for screen-detected cancers. CONCLUSION: Significant variation by health region in both the percentage of women with screen-detected cancer and the length of the diagnostic interval for screen and symptom-detected breast cancers suggests there could be important differences in local breast cancer diagnostic care coordination.
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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 | 0.000 |
| 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 | 0.000 |
| 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".