Proportional Frequency and Clinical Characteristics of Gastric, Esophageal, and Gastroesophageal Junction Cancers in a Private Medical Group in Brazil
Bibliographic record
Abstract
Background: Gastric cancer (GC) and esophageal cancer (EC) are among the most prevalent malignancies globally and are leading causes of cancer-related mortality. Gastroesophageal junction cancer (GEJC) is considered a distinct entity with specific histopathological characteristics. This study aimed to determine the proportional frequency of GEJC; the clinicopathological characteristics of GC, EC, and GEJC; and adherence to clinical diagnostic guidelines using data from a large private healthcare network in Brazil. Methods: In this retrospective, cross-sectional, descriptive study and database review, records from a Brazilian medical group (Dasa) were evaluated for adults (aged ≥ 18 years) who underwent upper digestive endoscopy between July 1, 2019, and June 30, 2022. Test results from the biopsy date associated with GC, EC, or GEJC diagnosis through December 2022 were collected retrospectively. Results: In total, 181 patients were evaluated, including 116 (64.1%) with GC, 37 (20.4%) with EC, 22 (12.2%) with GEJC, and six (3.3%) with lesions in ≥ 2 locations of interest. The ratio of GEJC to GC cases was 0.190, and the ratio of GEJC to EC cases was 0.595. Median (interquartile range) age at diagnosis was 67 (60 - 76) years, and most patients (57.5%) were aged 60 - 79 years. Sixty-two (53.4%), 27 (73.0%), 17 (77.3%), and four (66.7%) patients with GC, EC, GEJC, and lesions in ≥ 2 locations of interest, respectively, were male. The most frequently observed Lauren subtype was intestinal type for both GC and GEJC (68 (58.6%) patients and 18 (81.8%) patients, respectively). Adenocarcinoma was the most frequent histologic subtype observed in patients with EC (n = 21; 56.8%). Among patients with GC, EC, or GEJC and immunohistochemical biomarker testing, human epidermal growth factor receptor 2 expression was positive in 2/28 (7.1%), 1/3 (33.3%), and 1/2 (50.0%) patients, respectively, and mismatch repair deficiency was detected in 2/28 (7.1%), 0/3 (0%), and 0/2 (0%) patients. No patients with GEJC were tested for programmed cell death ligand 1 or claudin 18.2. Conclusions: An improved understanding of GEJC characteristics in Brazil is essential for facilitating early diagnosis, identifying optimal treatment strategies, and informing public health policies. The extremely low rates of biomarker testing in this study revealed a significant gap in the implementation of modern oncology guidelines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".