Optimal Management of Gastric Cancer
Bibliographic record
Abstract
OBJECTIVE: Defining processes of care, which are appropriate and necessary for management of gastric cancer (GC), is an important step toward improving outcomes. METHODS: Using a RAND/UCLA Appropriateness Method, an international multidisciplinary expert panel created 22 statements reflecting optimal management. All statements were scored for appropriateness and necessity. RESULTS: The following tenets were scored appropriate and necessary: (1) preoperative staging by computed tomography of abdomen/pelvis; (2) positron-emission tomographic scans not routinely indicated; (3) consideration for adjuvant therapy; (4) further clinical trials; (5) multidisciplinary decision making; (6) sufficient support at hospitals; (7) assessment of 16 or more lymph nodes (LNs); (8) in metastatic disease, surgery only for palliation of major symptoms; (9) surgeons experienced in GC management; (10) and surgeons experienced in both GC management and advanced laparoscopic surgery for laparoscopic resection. The following were scored appropriate, but of indeterminate necessity: (1) diagnostic laparoscopy before treatment; (2) a multidisciplinary approach to linitis plastica; (3) genetic assessment for diffuse GC and family history, or age less than 45 years; (4) endoscopic removal of select T1aN0 lesions; (5) D2 LN dissection in curative intent cases; (6) D1 LN dissection for early GC or patients with comorbidities; (7) frozen section analysis of margins; (8) nonemergent cases performed in a hospital with a volume of more than 15 resections per year; and (9) by a surgeon with more than 6 resection per year. CONCLUSIONS: The expert panel has created 22 statements for the perioperative management of GC patients, to provide guidance to clinicians and improve the care received by patients.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".