Variability of waiting times for the 4 most prevalent cancer types in Ontario: a retrospective population-based analysis
Bibliographic record
Abstract
BACKGROUND: Longer waiting times in cancer care are associated with lower care quality and wait-related patient dissatisfaction. We analyzed the variability and median of waiting times from when a patient seeks care to first treatment for the 4 most prevalent cancer types in Ontario. METHODS: Using retrospective health administrative data, we identified patients with a new diagnosis of prostate, breast, lung or colorectal cancer in Ontario between 2002 and 2012. Treatment interventions were categorized as chemotherapy, radiotherapy or surgery. We used regression analyses to calculate trends for the coefficient of variation, the Gini coefficient and the median waiting time for each cancer type-treatment type pair over the study period. RESULTS: During the study period, 95 501 new cases of prostate cancer, 89 244 breast cancer cases, 82 604 lung cancer cases and 80 761 colorectal cancer cases were registered. The coefficient of variation and the Gini coefficient of waiting times decreased for all cancer type-treatment type pairs (except for the Gini coefficient for breast cancer-radiotherapy) over the study period. However, both decreasing and increasing trends in median waiting times were observed across cancer type-treatment type pairs. INTERPRETATION: The variability of waiting time to first treatment for patients with prostate, breast, lung or colorectal cancer decreased between 2002 and 2012, which indicates improvements in equity in access to cancer care. This trend aligns with provincial efforts to improve access to and the efficiency of cancer care treatment in Ontario. The lack of consistent decreases in median waiting time highlights the need to identify improvement opportunities for cancer type-treatment type pairs with increasing median waiting times.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".