Incidental Colorectal Computed Tomography Abnormalities: Would You Send every Patient for a Colonoscopy?
Bibliographic record
Abstract
BACKGROUND: The clinical significance of colorectal wall thickening (CRWT) in patients undergoing abdominal computed tomography (CT) has not yet been definitively established. OBJECTIVES: To compare alleged findings on abdominal CT with those of a follow-up colonoscopy. METHODS: Ninety-four consecutive patients found to have large-bowel abnormalities on abdominal CT were referred for colonoscopy. Of these patients, 48 were referred for a suspected colorectal tumour and 46 for CRWT. Colonoscopy was performed and findings were compared. RESULTS: Of the 48 suspected colorectal tumours, 34 were determined to be neoplastic lesions on colonoscopy. Of these, 26 were malignant and eight were benign. Colonoscopy revealed no abnormality in 30 of 46 patients with CRWT as a solitary finding, and revealed some abnormality in 16 patients (12 had diverticular disease, four had benign neoplastic lesions). CONCLUSIONS: CRWT as an incidental and solitary finding on CT should not be regarded as a pathology prompting a colonoscopy. Approximately two-thirds of the patients had a normal colonoscopy and the remaining patients had benign lesions (12 had diverticular disease and four had benign neoplastic lesions). However, many of these patients seem to warrant colonoscopy regardless of CT findings, particularly patients who have a family history of colorectal cancer, have positive fecal occult blood test results or who are older than 50 years of age.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".