Detection of colorectal polyps and cancers with air enema MR colonography
Bibliographic record
Abstract
Objective To evaluate the sensitivity of air enema three-dimensional Fourier transform fast spoiled gradient-recalled(FSPGR) MR colonography in the detection of colorectal polyps and cancers.Methods Thirty patients scheduled for optical colonoscopy due to rectal bleeding,positive fecal occult blood test results or altered bowel habits underwent air enema three-dimensional Fourier transform FSPGR MR colonography and optical colonoscopy.Taking optical colonoscopy and histopathological examinations as standards,the sensitivities of air enema three-dimensional Fourier transform fast spoiled gradient-recalled(FSPGR) MR colonography in the detection of colorectal polyps and cancers were statistically analyzed according to the size of lesions.Results Seventy-six colorectal polyps and cancers were detected with optical colonoscopy,including 1-5 mm polyps(n=11),6-9 mm polyps(n=29) and ≥10 mm polyps and cancers(n=36) in diameter.The detection sensitivity of 1-5 mm polyps,6-9 mm polyps,≥10 mm polyps and cancers,≥6 mm polyps and cancers with MR colonography was 9.09%,75.86%,100% and 89.23%,respectively,and the overall detection sensitivity of all sizes colorectal polyps and cancers was 77.63%.Conclusion Detection sensitivity of air enema three-dimensional Fourier transform FSPGR MR colonography is low for 1-5 mm colorectal polyps,good for ≥6 mm polyps and cancers,and excellent for all polyps and cancers ≥10 mm.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".