Impact of colonoscopically missed cancers on patient outcomes.
Bibliographic record
Abstract
404 Background: There is increasing recognition of the potential for cancers to be missed on colonoscopy, but little is known about their outcomes. The objective of this study was to evaluate the outcomes of missed cancers relative to those detected by colonoscopy. Methods: We conducted a retrospective population-based cohort study, including all patients diagnosed with colorectal cancer (CRC) in Ontario, Canada from 2003-2009, who had undergone a colonoscopy within 36 months prior to diagnosis. Using previously defined time windows, we defined detected cancers as those diagnosed within 6 months of index colonoscopy and missed cancers as those diagnosed between 6 and 36 months after index colonoscopy. Patient and tumor factors were recorded as covariates. The primary outcome was overall survival, with secondary outcomes of resection rate, emergency presentation, and surgical complication rate. Logistic regression was used to analyze binary outcomes and Kaplan-Meier and Cox regression analyses were used for survival outcomes. Results: Overall, 30,475 patients were included in the study, with 2,804 being classified as having a missed cancer. Factors associated with missed cancers included colonic location (vs. rectum), older age, female sex, rural residence, and Charlson comorbidity score. Absolute 5-year overall survival was significantly lower in missed compared to detected cancers (60.8% vs. 68.3%, difference: 7.5%, p < 0.0001). In multivariable analysis, patients with missed cancers had a 22% higher hazard of death (HR: 1.22, 95% CI: 1.15 to 1.30, p < 0.0001). Patients with missed cancers were significantly more likely to present emergently with obstruction, bleeding or perforation (OR: 2.86, 95% CI: 2.56 to 3.13, p < 0.001) and were significantly less likely to have their tumors surgically resected (OR: 0.61, 95% CI: 0.55 to 0.67, p < 0.001). Conclusions: CRCs that are missed on initial colonoscopy are associated with markedly inferior patient outcomes, with a higher risk of emergent presentation, a lower likelihood of surgical resection, and most notably a significantly worse overall survival. These findings reinforce the critical importance of studying and improving quality measures of CRC screening to improve patient outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".