Motion-Computerized Tomographic Colography is a Better Method for Screening for Polyps: Arguments for the Motion
Bibliographic record
Abstract
Colorectal cancer is an important public health problem that is amenable to prevention and early treatment. Traditional screening techniques - fecal occult blood testing, flexible sigmoidoscopy, barium enema and colonoscopy - each have limitations in terms of diagnostic accuracy, cost and/or patient acceptability. Compliance with recommendations for screening has been poor, in part, because of negative perceptions about the available modalities. Virtual colonoscopy, or computerized tomographic colography, is a minimally invasive technique that safely evaluates the entire colon and does not require sedation. Thorough cleansing as well as immobilization and air insufflation of the colon is crucial to a successful examination. Sensitivity and specificity rates are reasonable, compared with conventional colonoscopy, and it has been shown that the latter technique can be averted in over two-thirds of cases, with few false-negative examinations. Most patients find virtual colonoscopy more acceptable than the conventional technique, and would prefer it if a repeat procedure were warranted. An economic analysis that found that computerized tomographic colography was less cost effective than conventional colonoscopy did not consider the indirect costs of the latter, which is an important limitation. Virtual colonoscopy is a novel radiological technique that may revolutionize screening for colorectal cancer.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".