Defining Surgical Quality in Gastric Cancer: A RAND/UCLA Appropriateness Study
Bibliographic record
Abstract
Brar, Savtaj MD, MSc; Law, Calvin MD, MPH; McLeod, Robin MD, FACS; Helyer, Lucy MD, MSc; Swallow, Carol MD, PhD, FACS; Paszat, Lawrence MD, MSc; Seevaratnam, Rajini MSc; Cardoso, Roberta RN, PhD; Dixon, Matthew MD; Mahar, Alyson MSc; Lourenco, Laercio G. MD; Yohanathan, Lavanya MD; Bocicariu, Alina MD; Bekaii-Saab, Tanios MD; Chau, Ian MD; Church, Neal MD; Coit, Daniel MD, FACS; Crane, Christopher H. MD; Earle, Craig MD, MSc; Mansfield, Paul MD, FACS; Marcon, Norman MD; Miner, Thomas MD, FACS; Noh, Sung Hoon MD; Porter, Geoff MD, MSc, FACS; Posner, Mitchell C. MD, FACS; Prachand, Vivek MD, FACS; Sano, Takeshi MD, PhD; van de Velde, Cornelis MD, PhD, FACS; Wong, Sandra MD, FACS; Coburn, Natalie MD, MPH, FACS Author Information
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".