Real‐world colorectal cancer diagnostic pathways in Ontario, Canada: A population‐based study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify colorectal cancer (CRC) diagnostic pathways and describe patients in those pathway groups. METHODS: This was a cross-sectional study of CRC patients in Ontario, Canada, diagnosed 2009-2012 that used linked administrative data at ICES. We used cluster analysis on 11 pathway variables characterising patient presentation, symptoms, procedures and referrals. We assessed associations between patient- and disease-related characteristics and diagnostic pathway group. We further characterised the pathways by diagnostic interval and number of related physician visits. RESULTS: Six diagnostic pathways were identified, with three adhering to provincial diagnostic guidelines: screening (N = 4494), colonoscopy (N = 10,066) and imaging plus colonoscopy (N = 3427). Non-adherent pathways were imaging alone (N = 2238), imaging and emergency presentation (N = 2849) and no pre-diagnostic workup (N = 887). Patients in adherent pathways were younger, had fewer comorbidities, lived in less deprived areas and had earlier stage disease. The median diagnostic interval length varied across pathways from 12 to 126 days, correlating with the number of CRC-related visits. CONCLUSIONS: This study demonstrated substantial variations in real-world CRC diagnostic pathways and 25% were diagnosed through non-adherent pathways. Those patients were older, had more comorbid disease and had higher stage cancer. Further research needs to identify and describe the reasons for divergent diagnostic processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".