Evaluating the utility of computed tomography of the chest for gastric cancer staging
Bibliographic record
Abstract
Background: International guidelines recommend routine computed tomography (CT) of the chest for gastric cancer staging. In Asian countries, where the incidence of pulmonary metastases is less than 1%, some guidelines recommend chest CT only for gastroesophageal junction cancers. If the incidence of pulmonary metastases is also low in Canada, routine chest CT may not be beneficial. Methods: We performed a retrospective review of patients in northern Alberta with newly diagnosed gastric cancer from January 2010 to July 2016. The primary aim of the study was to determine the incidence of pulmonary metastases at the time of diagnosis in this population. A secondary aim was to identify potential predictors of pulmonary metastases. We reviewed CT reports for pulmonary metastases. Imaging data also included liver metastases, abdominal lymphadenopathy (> 1 cm), ascites and omental or peritoneal nodules. Other data recorded were age, sex, primary tumour location, histologic type and tumour grade. Results: Four hundred and sixty-two patients (311 men, 151 women) were included in the analysis. Pulmonary metastases were identified in 25 patients (5.4%) overall and in 11 of 299 patients (3.7%) whose primary cancer was not in the cardia. On univariate analysis the presence of liver metastases (odds ratio [OR] 7.72, 95% confidence interval [CI] 3.24–18.37, p < 0.001) and abdominal lymphadenopathy (OR 3.30, 95% CI 1.29–8.48, p = 0.01) was associated with an increased risk of pulmonary metastases. Liver metastases retained statistical significance on multivariate analysis (OR 6.17, 95% CI 2.53–15.03, p < 0.001). Conclusion: The incidence of pulmonary metastases at the time of gastric cancer diagnosis is higher in northern Alberta than previously reported in Asian studies. Abdominal lymphadenopathy and liver metastases confer an elevated risk of pulmonary metastases.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".