Factors associated with mode of colorectal cancer detection and time to diagnosis: a population level study
Bibliographic record
Abstract
BACKGROUND: Although it is well-known that early detection of colorectal cancer (CRC) is important for optimal patient survival, the relationship of patient and health system factors with delayed diagnosis are unclear. The purpose of this study was to identify the demographic, clinical and healthcare factors related to mode of CRC detection and length of the diagnostic interval. METHODS: All residents of Alberta, Canada diagnosed with first-ever incident CRC in years 2004-2010 were identified from the Alberta Cancer Registry. Population-based administrative health datasets, including hospital discharge abstract, ambulatory care classification system and physician billing data, were used to identify healthcare services related to CRC diagnosis. The time to diagnosis was defined as the time from the first CRC-related healthcare visit to the date of CRC diagnosis. Mode of CRC detection was classified into three groups: urgent, screen-detected and symptomatic. Quantile regression was performed to assess factors associated with time to diagnosis. RESULTS: 9626 patients were included in the study; 25% of patients presented as urgent, 32% were screen-detected and 43% were symptomatic. The median time to diagnosis for urgent, screen-detected and symptomatic patients were 6 days (interquartile range (IQR) 2-14 days), 74 days (IQR 36-183 days), 84 days (IQR 39-223 days), respectively. Time to diagnosis was greater than 6 months for 27% of non-urgent patients. Healthcare factors had the largest impact on time to diagnosis: 3 or more visits to a GP increased the median by 140 days whereas 2 or more visits to a GI-specialist increased it by 108 days compared to 0-1 visits to a GP or GI-specialist, respectively. CONCLUSION: A large proportion of CRC patients required urgent work-up or had to wait more than 6 months for diagnosis. Actions are needed to reduce the frequency of urgent presentation as well as improve the timeliness of diagnosis. Findings suggest a need to improve coordination of care across multiple providers.
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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 | 0.001 | 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) | 0.000 | 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".