Wait times for breast cancer surgery in lean and traditional care pathways: a multi-group path analysis
Bibliographic record
Abstract
BACKGROUND: Research examining the impact of different models of care on wait times for breast cancer surgery indicates that organized assessment can reduce wait times, but few researchers have explored different care approaches between care sites serving a mixture of urban and rural patients and breast cancer care outcomes, especially within the Canadian context. Therefore, we sought to explore whether wait times from mammogram to surgery differed between lean referral and traditional referral pathways and what areas of inefficiencies need to be addressed. METHODS: We used a retrospective case review design and collected information on female patients (aged ≥ 19 yr) with stage 0-III breast cancer who were surgically treated between February 2016 and July 2019. RESULTS: = 248), with an average wait time difference of 9.3 days. For both care pathways, receiving a screening mammogram, living farther from the hospital, and receiving magnetic resonance imaging (MRI) increased wait times to surgery. CONCLUSION: Conducting the biopsy immediately after an abnormal mammogram, improving wait times for MRIs, and improving access for rural patients may be important areas of change-related focus. Shorter wait times to breast cancer surgery in the lean pathway support the advantage of a referral system whereby organized navigated systems coordinate all aspects of diagnosis and treatment.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".