Can Computed Tomographic Gastrography and Multiplanar Reformatting Aid the Laparoscopic Surgeon in Planning a Gastric Resection? A Pictorial Essay
Bibliographic record
Abstract
PURPOSE: To assess the value and feasibility of computed tomographic gastrography and multiplanar reformatting in the preoperative evaluation of patients undergoing laparoscopic gastric resection. MATERIALS AND METHODS: Fourteen patients with gastric lesions were included in the study. A supine scan was performed after a hypotonic drug, an effervescent agent, and intravenous contrast. This was followed by delayed prone and decubitus scans. We created multiplanar reformats, transparency rendered images, and endoluminal images. The tumours were localized, and distances were measured to the esophagogastric junction and the pylorus. RESULTS: Eleven patients underwent resections. Seven had laparoscopic wedge resections for aberrant pancreas (1 patient), carcinoid (1), Castleman disease (1), and gastrointestinal stromal tumours (GISTs) (4). One patient had an open subtotal gastrectomy for carcinoma due to adhesions. One had a hand-assisted sleeve resection for a gastrointestinal stromal tumour. Two had hand-assisted total gastrectomies for carcinoma and a GIST. For surgical planning, the surgeon rated the imaging extremely useful in 7 and useful in 4. Imaging was extremely useful or useful to localize laparoscopically invisible tumours in 6 patients and to relate tumours to the esophagogastric junction or pylorus and to assess localized vs extensive resection in 8. Correlation was excellent between the preoperative imaging and the intraoperative findings. CONCLUSIONS: Computed tomographic gastrography and multiplanar reformatting are useful aids in preoperative planning of laparoscopic gastric resections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".