Staging and Surgical Approaches in Gastric Cancer: A Clinical Practice Guideline
Bibliographic record
Abstract
BACKGROUND: Resection is the cornerstone of cure for gastric adenocarcinoma; however, several aspects of surgical intervention remain controversial or are suboptimally applied at a population level, including staging, extent of lymphadenectomy (lnd), minimum number of lymph nodes that have to be assessed, gross resection margins, use of minimally invasive surgery, and relationship of surgical volumes with patient outcomes and resection in stage iv gastric cancer. METHODS: Literature searches were conducted in databases including medline (up to 10 June 2016), embase (up to week 24 of 2016), the Cochrane Library and various other practice guideline sites and guideline developer Web sites. A practice guideline was developed. RESULTS: One guideline, seven systematic reviews, and forty-eight primary studies were included in the evidence base for this guidance document. Seven recommendations are presented. CONCLUSIONS: All patients should be discussed at a multidisciplinary team meeting, and computed tomography (ct) imaging of chest and abdomen should always be performed when staging patients. Diagnostic laparoscopy is useful in the determination of M1 disease not visible on ct images. A D2 lnd is preferred for curative-intent resection of gastric cancer. At least 16 lymph nodes should be assessed for adequate staging of curative-resected gastric cancer. Gastric cancer surgery should aim to achieve an R0 resection margin. In the metastatic setting, surgery should be considered only for palliation of symptoms. Patients should be referred to higher-volume centres and those that have adequate support to manage potential complications. Laparoscopic resections should be performed to the same standards as those for open resections, by surgeons who are experienced in both advanced laparoscopic surgery and gastric cancer management.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".