Circumstances in which colonoscopy misses cancer: Table 1
Bibliographic record
Abstract
Colonoscopy is associated with a varying risk of missing colorectal cancer (CRC). The objective of this paper was to review the existing evidence that indicates when colonoscopy may miss cancer in usual clinical practice and to provide information that would be helpful to endoscopists in their daily practice. CRC is diagnosed within 3 years in about 5% of persons with CRC who undergo colonoscopy in whom the cancer is not detected. Future research should be directed at disentangling the relative contributions of tumour biology and colonoscopy quality in explaining this result. When consent is obtained for colonoscopy, patients must be informed of the small risk that a cancer may not be detected. Steps that can be taken to address colonoscopy quality include formal training in colonoscopy and polypectomy technique, coupled with maintenance of skills by performing at least 300 colonoscopies per year. The use of split dose bowel preparation is advised. Colonoscopy should be completed to the caecum with documentation of landmarks (ileocaecal valve; appendiceal orifice). Careful colonoscopy technique includes examining the proximal sides of flexures and folds, washing and suctioning debris and ensuring adequate colonic distension. Caecal intubation and adenoma detection rates should be reported and reviewed. Lesions should be completely removed at polypectomy and attention given to appropriate surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".