Motion - Colonoscopic Surveillance is More Cost Effective than Colectomy in Patients with Ulcerative Colitis: Arguments for the Motion
Bibliographic record
Abstract
Patients with ulcerative colitis (UC) are at increased risk for colorectal cancer (CRC), especially those with longstanding disease, pancolitis or primary sclerosing cholangitis. The incidence of colitis- associated cancer is increasing, and the mortality rates from CRC are higher in UC patients than in the general population. Case control studies have demonstrated that surveillance colonoscopy reduces the risk of dying from CRC. A well conducted decision analysis found that surveillance colonoscopy decreases cancer-related mortality and increases life expectancy. The results with surveillance programs were almost as good as with prophylactic colectomy. A subsequent cost effectiveness analysis using the same model found that, compared with a policy of no surveillance, colonoscopic surveillance was more effective at preventing death from CRC and was less costly. The best strategy appears to be to perform colonoscopies every three years. The analysis also showed that colectomy should be recommended in patients with low-grade dysplasia. Patients at very high risk for CRC should undergo yearly colonoscopy, and patients who are concerned about the limitations of this technique should be offered prophylactic colectomy.
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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".