Wait times for colorectal cancer patients in a universal healthcare system over a decade: Is it sustainable?
Bibliographic record
Abstract
697 Background: Delays in treatment for colorectal cancer (CRC) may worsen prognosis and increase patient anxiety. This study aims to understand population-based trends and variations in wait times (WTs) for CRC in a universal healthcare system over a decade. Methods: Patients diagnosed with stage I-IV CRC in Manitoba, Canada between 2004 and 2014 were included. Data were obtained through province-wide administrative claims and cancer registry. WTs were defined as time from index contact to pathological diagnosis (diagnosis WT), time from pathological diagnosis to first treatment (treatment WT) and total (diagnosis + treatment WT). Index contact was the consult preceding the first gastrointestinal investigation in the year preceding the date of diagnosis. First treatment was radiation, chemotherapy or surgery. The association between WTs and year of diagnosis was estimated using Negative Binomial regression and reported as incidence rate ratio (IRR). Variability in WTs by year were estimated using the Coefficient of Variation (CV) and average annual percent change (AAPC). A CV > 100 indicates high-variability and < 100 indicates low-variaability. Results: A total of 5359 patients were diagnosed with CRC (1802 rectal vs 3557 colon). WTs increased overall. Total WTs for rectal cancer increased by 6% (IRR 1.06, 95% CI 1.04-1.07, p < 0.01) per year from a median of 90 days in 2004 to 147 days in 2014. This was due increases in time to diagnosis (IRR 1.07, 95% CI 1.06-1.09, p < 0.01) and treatment (IRR 1.04, 95% CI 1.03-1.06, p < 0.01). Total colon cancer WTs increased an estimated 5% (IRR 1.05, 95% CI 1.04-1.06, p < 0.01) per year from a median of 89 days in 2004 to 110 days in 2014. This was due to both time to diagnosis (IRR 1.05, 95% CI 1.04-1.07, p < 0.01) and treatment (IRR 1.03, 95% CI 1.02-1.04, p < 0.01). There was increasing variability in total WTs. The CV increased from 87 in 2004 to 102 in 2014 in rectal cancer (AAPC +3.85%) and from 86 in 2004 to 128 in 2014 in colon cancer (AAPC + 5.04%). Conclusions: Total WTs for CRC in Manitoba have increased from 2004 and 2014. This may reflect the growing challenges in providing increasingly complex cancer care to geographically dispersed populations in a universal healthcare system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".