Colonoscopy resource availability and its association with the colorectal cancer diagnostic interval: A population‐based cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Colonoscopy is a key resource used to diagnose colorectal cancer (CRC). This study evaluated the relationship between colonoscopy availability and the length of the CRC diagnostic interval. METHODS: This is a cross-sectional study of CRC patients diagnosed in Ontario, Canada, in 2008-2012. We used administrative health data to characterise colonoscopist density, private colonoscopy clinic access, distance to the closest colonoscopist and the diagnostic interval, defined as the time from patients' first cancer-related healthcare encounter to their cancer diagnosis date. We used multivariable quantile regression to evaluate the association between colonoscopy availability and the diagnostic interval, modelling the median and 90th percentile. RESULTS: The median diagnostic interval was 84 days (90th percentile 323 days). The diagnostic interval was longer in patients residing in areas with lower colonoscopists density or private clinic access (adjusted median difference = 9 and 19 days, respectively), with evidence of effect modification by symptom status. Increased distance to a colonoscopist was associated with a longer diagnostic interval in asymptomatic patients, but a shorter diagnostic interval in symptomatic patients (adjusted median difference = 29 and -25 days, respectively). CONCLUSIONS: This study demonstrated that reduced colonoscopy resource availability is associated with longer diagnostic intervals for CRC patients.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".