Clinical and endoscopist factors associated with post‐colonoscopy colorectal cancer in a population‐based sample
Bibliographic record
Abstract
AIM: Factors associated with verified post-colonoscopy colorectal cancers (PCCRC) have not been well defined and survival for these patients is not well described. We aimed to assess the association of patient, tumour and endoscopist characteristics with PCCRC. METHODS: Using population-based data, we identified individuals diagnosed with CRC from 1 January 2000 to 31 December 2005 who underwent a colonoscopy within 3 years prior to diagnosis. Detected cancers were those diagnosed ≤6 months following colonoscopy; PCCRC were diagnosed >6 months to ≤3 years following colonoscopy. Post-colonoscopy and detected cancers were verified through chart review using a hospital-based simple random sampling frame. We used multivariable conditional logistic regression to determine the association of patient, tumour and endoscopist factors with PCCRC and compared overall survival using Cox proportional hazard models. RESULTS: Using the random sampling frame, we identified 498 patients with PCCRC and 498 with detected CRC; we obtained records and confirmed 367 patients with PCCRC and 412 with detected cancers. In multivariable analysis, patient age (OR 1.01; 95% CI 1.00-1.03) and tumour location (distal vs. proximal OR 0.36; 95% CI 0.25-0.53) were associated with PCCRC; endoscopist quality measures were not significantly associated with PCCRC. We did not find significant differences in overall survival between PCCRC and detected cancers (hazard ratio 1.12; 95% CI 0.92-1.32). CONCLUSION: Although endoscopic quality measures are important for CRC prevention, endoscopist factors were not associated with PCCRC. This study highlights the need for further research into the role of tumour biology in PCCRC development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".