Characteristics of Interval Colorectal Cancer: A Canadian Retrospective Population-Level Analysis from Newfoundland and Labrador
Bibliographic record
Abstract
Interval colorectal cancers (I-CRCs) arise during the interval time period between scheduled colonoscopies. Predicting which patients are at risk of I-CRCs remains an elusive undertaking, but evidence would suggest that most I-CRCs arise from lesions missed on index endoscopy. The procedural factors that lead to missed lesions are numerous and lack consensus in the literature. In Canada, the province of Newfoundland and Labrador has the highest incidence of CRCs. In this study our aim was to examine I-CRCs (3-60 months after last colonoscopy) in NL through a population-level analysis covering 67% of the province from 2001-2018. We estimated the I-CRC rate to be up to 9.3%. Median age of I-CRC diagnosis was 67.1 years with an interval time of 2.9 years. About 57% of these tumors occurred proximal to the splenic flexure, with 53% presenting as local disease. No temporal differences were observed in interval time or tumor distribution. On univariate and multivariable logistical regression, risk of right-sided I-CRC did not correlate to the index colonoscopy indication, bowel preparation quality, size of largest polyp removed, colonoscopy completion rate, or stage at presentation. Improvements in synoptic reporting utilization and national registries are needed to identity risk factors and reduce I-CRC frequency.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".