Assurance of Timely Access to Breast Cancer Diagnosis and Treatment by a Regional Breast Health Clinic Serving Both Urban and Rural-Remote Communities
Bibliographic record
Abstract
In response to breast cancer diagnostic regional wait times exceeding both national and provincial standards and to symptomatic patient referrals for diagnostic mammography taking longer than abnormal screening mammography referrals, the Rae Fawcett Breast Health Clinic (RFBHC) was opened in 2017 in a mid-sized Canadian hospital serving both urban and rural-remote communities. We investigated whether the RFBHC improved wait times to breast cancer diagnosis, improved compliance with national and provincial breast cancer standards, and decreased the wait time disparity associated with referral source. Statistical analyses of wait time differences were conducted between patients who were diagnosed with breast cancer prior to and after the RFBHC establishment. Study group compliance with national and provincial standards and wait time differences by referral source were also analysed. A survey was administered to assess overall patient experience with the RFBHC and clinic wait times. RFBHC patients had a shorter mean wait to breast cancer diagnosis (24.4 vs. 45.7 days, p ≤ 0.001) and a shorter mean wait to initial breast cancer treatment (49.1 vs. 78.9 days, p ≤ 0.001) than pre-RFBHC patients. After the RFBHC establishment, patients who attended the RFBHC had a shorter mean wait time to breast cancer diagnosis (24.4 vs. 36.9 days, p = 0.005) and to initial treatment (49.1 vs. 73.1 days, p ≤ 0.001) than patients who did not attend the clinic. Compliance with national and provincial breast cancer standards improved after the RFBHC establishment and the wait time disparity between screening mammography referrals and symptomatic patient referrals decreased. Survey results indicate that the RFBHC is meeting patient expectations. We concluded that the establishment of a breast health clinic in a Canadian center serving urban and rural-remote communities improved breast diagnostic services.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".