Double-contrast Magnetic Resonance Imaging in Preoperative Evaluation of Rectal Cancer: Use of Aqueous Jelly as Luminal Contrast
Bibliographic record
Abstract
Carcinoma of the rectum is one of the most common malignant tumours in the Western world. Preoperative knowledge of the local spread of rectal cancer is essential in the determination of the therapeutic strategy for this disease. Magnetic resonance imaging (MRI) has been described as being accurate in the detection of the extent of rectal cancer for staging. In particular, double-contrast material enhanced MRI of the rectum has been reported to better delineate tumours against the lumen and the normal mucosa [1]. Tissue characterization is excellent with MRI and can be improved further for staging of rectal cancer by enhancement of the mucosa with intravenously administered gadoliniumbased contrast combined with intraluminal contrast material to distend the bowel. Tumour detection and assessment of penetration through the rectal wall is important information for the surgeon [2]. Piippo et al [3] concluded that tumour detection may be problematic in an undistended bowel and may result in extended examination time and inadequate imaging planes that are not perpendicular to the tumour to allow accurate analysis. Several investigators have advocated the use of rectal contrast medium for improving the delineation of tumour and to estimate its extent within the rectum [3e7]. Contrast agents that have been used are superparamagnetic iron oxide solutions, methylcellulose, and barium suspensions [5]. Herein, we describe our experience with aqueous gel as intraluminal contrast material for preoperative staging of rectal 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".