ICES Report: New Findings about the Risks and Limitations of Colonoscopy Used in the Early Detection of Colorectal Cancer
Bibliographic record
Abstract
Colonoscopy has established benefits for the detection and prevention of colorectal cancer, which is the second leading cause of cancer-related deaths in Canada and the United States. However, two recently published studies from scientists at the Institute for Clinical Evaluative Sciences (ICES) have found that the procedure has certain limitations (Baxter et al. 2009; Rabeneck et al. 2008). First, colonoscopy seems to be less effec-tive at preventing cancer deaths from tumours that originate in the right side of the large bowel. Second, while the procedure is safe for many patients, certain people appear to be at increased risk for serious complications. Colonoscopy is widely used to detect both colorectal cancer and adenomatous polyps, which may become malignant if left alone. During a complete colonoscopy, a physician - usually a gastroenterologist or general surgeon - inserts a long, flexible tube called a colonoscope through the patient's rectum and along the length of the large bowel. The goal is to scan the entire colon for potentially cancerous or pre-cancerous growths. If such a polyp or lesion is detected, it can often be removed during the colonoscopy so that no additional procedures or surgery are needed. The findings from the two new ICES studies, detailed below, are especially important given the widespread and increasing use of colonoscopy and the need to evaluate possible harms from and limitations of the procedure.
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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 | 0.033 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".