Effect of specialized diagnostic assessment units on the time to diagnosis in screen-detected breast cancer patients
Bibliographic record
Abstract
BACKGROUND: The duration of the cancer diagnostic process has considerable influence on patients' psychosocial well-being. Breast diagnostic assessment units (DAUs) in Ontario, Canada are designed to improve the quality and timeliness of care during a breast cancer diagnosis. We compared the diagnostic duration of patients diagnosed through a DAU vs usual care (UC). METHODS: Retrospective population-based cohort study of 2499 screen-detected breast cancers (2011) using administrative health-care databases linked to the Ontario Cancer Registry. The diagnostic interval was measured from the initial screen to cancer diagnosis. Diagnostic assessment unit use was based on the biopsy and/or surgery hospital. We compared the length of the diagnostic interval between the DAU groups using multivariable quantile regression. RESULTS: Diagnostic assessment units had a higher proportion of patients diagnosed within the 7-week target compared with UC (79.1% vs 70.2%, P<0.001). The median time to diagnosis at DAUs was 26 days, which was 9 days shorter compared with UC (95% CI: 6.4-11.6). This effect was reduced to 8.3 days after adjusting for all study covariates. Adjusted DAU differences were similar at the 75th and 90th percentiles of the diagnostic interval distribution. CONCLUSIONS: Diagnosis through an Ontario DAU was associated with a reduced time to diagnosis for screen-detected breast cancer patients, which likely reduces the anxiety and distress associated with waiting for a diagnosis.
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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) | 0.001 | 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) | 0.001 | 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 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".