Measurement of clinical delay intervals among younger adults with colorectal cancer using health administrative data: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Clinical delays may be important contributors to outcomes among younger adults (<50 years) with colorectal cancer (CRC). We aimed to describe delay intervals for younger adults with CRC using health administrative data to understand drivers of delay in this population. METHODS: This was a population-based study of adults <50 diagnosed with CRC in Ontario, Canada from 2003 to 2018. Using administrative code-based algorithms (including billing codes), we identified four time points along the pathway to treatment-first presentation with a CRC-related symptom, first investigation, diagnosis date and treatment start. Intervals between these time points were calculated. Multivariable quantile regression was performed to explore associations between patient and disease factors with the median length of each interval. RESULTS: 6853 patients aged 15-49 were diagnosed with CRC and met the inclusion criteria. Males comprised 52% of the cohort, the median age was 45 years (IQR 40-47), and 25% had stage IV disease. The median time from presentation to treatment start (overall interval) was 109 days (IQR 55-218). Time between presentation and first investigation was short (median 5 days), as was time between diagnosis and treatment start (median 23 days). The greatest component of delay occurred between first investigation and diagnosis (median 78 days). Women, patients with distal tumours, and patients with earlier stage disease had significantly longer overall intervals. CONCLUSIONS: Some younger CRC patients experience prolonged times from presentation to treatment, and time between first investigation to diagnosis was an important contributor. Access to endoscopy may be a target for intervention.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".