Yield of routine staging laparoscopy in patients with gastric cancer in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Despite guidelines recommending diagnostic laparoscopy in patients with gastric cancer, implementation is low. We aimed to explore trends in the use of laparoscopy for staging of gastric cancer in Alberta, Canada, determine the rate of positive findings and identify factors predictive of positive staging laparoscopy (SL) findings in this patient population. METHODS: In August 2018, we sent a survey to all general surgeons in Alberta who were members of the Alberta Association of General Surgeons to identify those treating gastric cancer. The survey inquired about type of practice (academic or community), gastric cancer case volume, routine versus selective use of SL and, if selective use of SL, criteria used to select cases. Participants were also asked to provide data from their SL cases from July 2007 to February 2019. We double-checked surgeon records with chart review. The primary outcome was evidence of metastatic disease on SL or cytologic examination or both. We performed logistic regression analysis to identify factors predictive of positive laparoscopy findings. RESULTS: The survey was completed by 41 of 127 surgeons (response rate 32.3%). We reviewed 116 cases from 5 surgeons at 4 centres. Gross metastatic disease or positive findings on cytologic examination or both were identified in 37 patients (31.9%). On univariate analysis, the following were associated with an increased risk of identification of metastatic disease at laparoscopy: visualization of the primary tumour on computed tomography (CT) (odds ratio [OR] 9.8, 95% confidence interval [CI] 1.2-76.5), presence of abdominal lymphadenopathy greater than 1 cm (OR 2.4, 95% CI 1.1-5.4) and presence of ascites (OR 19.1, 95% CI 2.2-161.8). Visualization of the primary tumour on CT (OR 8.4, 95% CI 1.0-68.3) and the presence of ascites (OR 15.9, 95% CI 1.8-137.0) remained statistically significant predictors on multivariate analysis. CONCLUSION: Metastatic disease was identified at SL in almost one-third of cases, which suggests that SL should still be used routinely in gastric cancer staging in Canadian centres. Our study identified several preoperative imaging findings associated with evidence of metastatic disease on laparoscopy; however, further studies are needed to establish robust predictors of positive findings before advocating for a selective SL approach.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".