Is being diagnosed at a dedicated breast assessment unit associated with a reduction in the time to diagnosis for symptomatic breast cancer patients?
Bibliographic record
Abstract
The length of the cancer diagnostic interval can affect a patient's survival and psychosocial well-being. Ontario Diagnostic Assessment Units (DAUs) were designed to expedite the diagnostic process through coordinated care. We examined the effect of DAUs on the diagnostic interval among female patients with symptomatic breast cancer in Ontario using the Ontario Cancer Registry linked to administrative healthcare data. The diagnostic interval was defined as the time from patients' first referral or test to the cancer diagnosis. DAU use was determined based on the hospital where the breast biopsy/surgery was performed. Multivariable quantile regression and logistic regression analyses adjusted for possible confounders. Forty-seven per cent of patients were diagnosed in a DAU and 53% in usual care (UC). DAUs achieved the Canadian timeliness targets more often than UC (71.7% vs. 58.1%, respectively). DAU use was associated with a 10-day (95% CI: 7.8-11.9) reduction in the median diagnostic interval. This effect increased to 19 days for patients at the 75th percentile and 22 days for those at the 90th percentile of the diagnostic interval distribution. Use of an Ontario DAU is associated with a shorter time to diagnosis in patients with symptomatic breast cancer, especially for those who would otherwise wait the longest.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".