Surveillance programmes for colorectal cancer in inflammatory bowel disease: have we got it right?
Bibliographic record
Abstract
While it is generally agreed that screening for colon cancer is a rational strategy in chronic colitis of ulcerative colitis or Crohn’s disease, there remains some debate over the approach to managing outcomes of dysplasia surveillance colonoscopy. While some have advocated for colectomy for low grade dysplasia,1–3 others have argued for more surveillance;4 while some have argued for polypectomy for adenoma-like masses (ALMs)5 others have shown the potential for disasterous outcomes if polypectomy is pursued where rigorous follow-up will not be sustained.6 However, there has been less discussion about the technical approach to dysplasia surveillance. In survey studies in the US and UK it was shown that surveillance colonoscopy frequency and biopsy protocols have varied widely.7 8 One study suggested that at least 33 biopsies were required to maximise dysplasia discovery,9 but this has never been revisited. To counter the problem of time and expense incurred with 30+ biopsies chromoendoscopy emerged as a means to target biopsies and otherwise minimise random multiple biopsies.10 11 In this issue of Gut , Lutgens et al ( see page 1246 ) address the very basic question of timing the initiation of dysplasia surveillance.12 Elsewhere, it has been identified that colon cancers may occur before 8 years of disease.13 14 However, in a countrywide assessment Lutgens et al have systematically attempted to discern how often cancers are missed if the starting points given in current surveillance guidelines are adhered to. In the British guidelines it is suggested that surveillance colonoscopy should be initiated at 8 years in extensive colitis and 15 years in left-sided colitis.15 The authors adequately discuss that this recommendation was based mostly on expert opinion rather than on firm data. For the most part, the 8 year starting point for extensive …
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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 | 0.023 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".