The Accuracy of Colorectal Cancer Detection by Computed Tomography in the Unprepared Large Bowel in a Community-Based Hospital
Bibliographic record
Abstract
PURPOSE: This retrospective study examined the performance of general radiologists in a community-based hospital in detecting colorectal cancer (CRC) with computed tomography (CT) in the unprepared large bowel. METHODS: The pathology database at a community hospital over the past 7 years (2009-2015) was retrospectively analysed for pathologically proven CRC (924 cases). The provincial hospital information profile for these patients was reviewed to determine if they had an abdominal CT for any reason in the year prior to biopsy. Metrics such as age, sex, time between the CT and biopsy or surgery, whether CRC was initially detected by the radiologist, and if this was an emergency presentation was evaluated. In the cases where CRC was not identified, the CT scans were reanalysed to determine if the CRC was identifiable in retrospect. The sensitivity of detecting CRC by CT scan in the unprepared large bowel was calculated. RESULTS: Of the 924 biopsy proven CRC cases, 22% (207 of 924) of the patients had a CT prior to biopsy. Of these cases, 47% (97 of 207) presented on an emergency basis. Of the cases with imaging in the year prior, about 60% (125 of 207) had cancer prospectively detected by the radiologist. Upon re-examination of the cases in which CRC was not initially detected, 59% were visualized in retrospect. CONCLUSIONS: Community general radiologists can successfully detect CRC with a high degree of accuracy. Reformatted images, bowel wall thickening when regional nodes are prominent, and minimizing oral contrast were helpful in improving detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".