Gastric Cancer in the Countryside or in the City: Does the Prognosis Change? An Analysis from the German States of Brandenburg and Berlin
Bibliographic record
Abstract
Background: Medical care structures differ between urban and rural areas. Clinical cancer registries can depict real-world care through detailed data analysis, identifying potential regional disparities and contributing to improvements in healthcare. Methods: Data from the Brandenburg–Berlin Clinical-Epidemiological Cancer Registry for the years of diagnosis 2017–2022 were analyzed to assess the epidemiology and real-world care of gastric cancer, including cardia. Brandenburg was compared to Berlin regarding perioperative treatment regimens. The resulting survival benefits were assessed using Kaplan–Meier and Cox regression models. Results: For the years of diagnosis 2017 to 2022, 5805 cases of gastric carcinoma were documented in the cancer registry. Survival data showed no significant differences between Berlin and Brandenburg. Preoperative therapy for cT3/cT4N0 tumors was significantly more common in Berlin (72%) than in Brandenburg (68%). Perioperative therapy was associated with a survival benefit for stages T3-/T4N+ but not for stages T1N+ or T2. The lower proportion of pre-treated T3/T4N+ patients in rural Brandenburg did not result in a significant survival difference. Conclusions: The data provide a comprehensive representation of the current state of gastric cancer care in these two regions. Gastric cancer treatment outcomes, in terms of survival, are comparable between the rural region of Brandenburg and the urban center of Berlin.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".