Cancer Treatment Patterns Among Yukon Residents Referred to British Columbia for Care: A 13-Year Retrospective Study
Bibliographic record
Abstract
Yukon residents often must travel long distances to access specialized cancer care, which may impact cancer treatment patterns. We conducted a retrospective study to characterize all adult breast, prostate, colorectal, and lung cancer cases from the Yukon, diagnosed from 2009 to 2021 and seen in consultation at BC Cancer. We collected data on demographics, tumour characteristics and treatment, including timepoints for cancer care. A secondary analysis of non-referred cases was conducted. There were a total of 336 breast, 270 prostate, 279 colorectal and 266 lung cancer cases diagnosed in the Yukon from 2009 to 2021, of which 298 (88.7%), 120 (44.4%), 206 (73.8%) and 204 (76.7%) cases were referred to BC Cancer, and 266 (79.2%), 118 (43.7%), 204 (73.1%) and 183 (68.8%) were included in this study, respectively. Most cases were diagnosed at an early stage (breast: 92.9%, prostate: 82.2%, colorectal: 72.1%, lung: 45.9%). Nearly 70% of cases resided in Whitehorse (Yukon's capital), where most Yukon residents live. Compared to available published Canadian timepoints, Yukon patients had similar or shorter wait times in 13 of 22 timepoints along the pathway to diagnosis and treatment. However, time from biopsy to surgery had the longest relative wait times across all tumour groups (range: 26-60% longer). Our study provides baseline data that can help inform cancer care provision for Yukon residents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".