What studies are appropriate and necessary for staging gastric cancer? Results of a RAND/UCLA expert panel.
Bibliographic record
Abstract
15 Background: The approach for staging gastric cancer (GC) patients has not been well defined, resulting in widespread heterogeneity in the application of pre-operative staging modalities. Methods: A multi-disciplinary expert panel of 16 physicians from 6 countries scored 84 scenarios using the RAND/UCLA Appropriateness Methodology. Appropriateness was scored from 1 (highly inappropriate) to 9 (highly appropriate). Median appropriateness scores (AS) from 1-3 were considered inappropriate, 4-6 uncertain, and 7-9, appropriate. Agreement was reached when 11 of 16 panelists scored the scenario similarly. If a scenario was agreed to be appropriate, it was given a necessity score (NS) in the same manner. AS and NS are reported if agreed upon. Results: TNM staging should be determined pre-operatively (AS 7.0-9.0; NS 7.0-9.0). Pre-operative radiological assessment should include a computed tomography (CT)−abdomen, CT−pelvis, and should be performed with a multi−detector CT scanner with 5 mm slices (AS 8.0−9.0; NS 7.0−9.0). A CT Chest may be performed (AS 7.5). The utility of a chest radiograph was indeterminate. All patients should have a pre-operative esophagogastroduodenoscopy (EGD). The endoscopist should biopsy the tumor; document its size, description, location, distance from the GE junction, and any GE junction, esophageal or duodenal involvement. If the EGD report is unclear, the surgeon should repeat it to confirm tumor location (AS 7.5-9.0, NS 7.0−9.0). Endoscopic ultrasound is appropriate prior to endoscopic resection, but not surgical resection (AS 9.0). Diagnostic laparoscopy (DL) should be performed prior to resection of cT3,4 lesions, or multi−visceral resections. DL should include visual inspection of the stomach, diaphragm, liver, and ovaries (AS 8.0−9.0, NS 7.0−9.0). Conclusions: The Gastric Cancer Processes of Care expert panel has made recommendations regarding pre−operative staging modalities in an effort to standardize work up. Standardization could lead to more accurate staging and allocation towards optimal stage−specific treatments.
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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 | 0.103 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".