Motion - Computerized Tomographic Colography is a Better Method for Screening for Polyps: Arguments Against the Motion
Bibliographic record
Abstract
Computerized tomographic (CT) colography is an exciting technique whereby images of the colonic wall and lumen can be obtained without colonoscopy. It is not as good as conventional colonoscopy, however, because of both inherent and performance limitations. Among the former is the inability to visualize subtle mucosal lesions, such as alterations in colour or pliability. More importantly, CT colography is strictly a diagnostic technique, and does not allow biopsy or removal of polyps. The vigorous bowel preparation required for this procedure can be very unpleasant for the patient, and includes purgatives followed by distension of the colon with air. Unlike with colonoscopy, adherent stool can be difficult to distinguish radiologically from polyps or cancers; as a result, many patients require colonoscopy anyway. The major performance limitations of CT colography are poor sensitivity and specificity compared with conventional colonoscopy. Rectal lesions, flat adenomas and diminutive adenomas are especially difficult to detect, and false-positive results are also common. In addition, the procedure is expensive and less cost effective than colonoscopy. CT colography takes relatively little patient time, but a substantial amount of time is needed for the radiologist to interpret the images. Interobserver variability is high. For all of these reasons, CT colography cannot be recommended as a screening test for colorectal neoplasia.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".