Motion - Colonoscopic Surveillance is more Cost Effective than Colectomy in Patients with Ulcerative Colitis: Arguments Against the Motion
Bibliographic record
Abstract
There are insufficient data upon which to base recommendations about surveillance colonoscopy and prophylactic colectomy for the prevention of colorectal cancer in patients with ulcerative colitis. Case series, analyses of intermediate results and extrapolations from other patient groups do not constitute reliable evidence. Available studies are susceptible to several biases: the 'healthy worker' effect, surveillance bias and selection bias. Patients who are enrolled in surveillance programs are more likely to be thoroughly evaluated beforehand, are more likely to be given a diagnosis of dysplasia or neoplasm even when asymptomatic and are more likely to comply with medical treatment, including maintenance anti-inflammatory medication. Comparisons of the rates of neoplasia or death between surveyed and nonsurveyed patients are, therefore, of questionable validity. Prophylactic colectomy, unlike surveillance colonoscopy, prevents death from colorectal cancer. Moreover, it is difficult to keep patients in surveillance programs, and those who withdraw from programs appear to be at high risk of developing cancer. Prophylactic colectomy should be strongly considered for patients with dysplasia, sclerosing cholangitis, longstanding pancolitis (especially if it began early in life) or a positive family history of colorectal cancer. This procedure is underused in clinical practice and is a good alternative to colonoscopic surveillance in high risk patients.
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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 | 0.024 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".