Improving the quality of surgery for gastric cancer—Are surgeons aware of the relevant quality indicators?
Bibliographic record
Abstract
4048 Background: Gastric cancer survival in the West is inferior to that achieved in Asian centers. While differences in tumor biology may play a role, poor quality surgery contributes to understaging. In our evaluation, adequate lymph node (LN) assessment (≥15) was achieved in only one third of patients and independently predicted survival across Surveillance, Epidemiology and End Results (SEER) regions (ASCO 2005, Abstract # 4004). The standard proximal margin of resection is recommended to be ≥5 cm; revision based on intraoperative frozen section is of benefit. We hypothesize that the majority of surgeons performing gastric cancer surgery in a North American setting are unaware of the recommended standards. Methods: Using the Ontario College of Physicians and Surgeons registry, surgeons who potentially include gastric cancer surgery in their scope of practice were identified. A questionnaire was mailed to 559, 55% responded. 203surgeons reported managing gastric cancer. Results were evaluated by chi-square and logistic regression; p<0.05 was considered significant. Results: 86% of respondents were male; 59% in urban non-academic practice, and 30% in academic. 42% of surgeons operate on 2–5 cases/yr and 18 % on >5. One third of surgeons identified ≤ 4 cm to be the desired proximal margin. Frozen section is used by 52% to evaluate proximal margin status. 20% were unsure of the number of LN needed to accuratly stage, the median number reported by the remainder was 10 (range 0 - 30). 99% refer for adjuvant therapy. This was less likely for patients in poor medical condition, poor nutritional status, or age >70 years. Young patients, those with bulky LN or positive margins were more likely to be referred. Overall, only 16 of 203identified the need for both a ≥5 cm proximal margin and ≥15LN; this was too small a group to analyze for demographic associations. Surgeons who do >5 gastric resections/yr were more likely to report performing a D2 resection (p = 0.008). Conclusions: The majority of surgeons operating on gastric cancer in Ontario did not identify standard quality indicators of gastric cancer surgery. A continuing medical education program should be designed to address the knowledge gap, aiming to improve the quality of surgery and outcome of multidisciplinary management. No significant financial relationships to disclose.
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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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".