Gastric Cancer: Correlation of Histologic Type with Commonly Used Prognostic Variables
Bibliographic record
Abstract
Background: Gastric cancer is one of the most common cancers among Iranian men and women. Objectives: The aim here was to investigate different histopathologic types and features of this cancer in association with selected prognostic variables. Methods: A retrospective cross-sectional study was performed to reevaluate the pathologic samples of 100 cases of gastric cancer referred to Shohadaye Tajrish Hospital, Tehran, Iran from 2017 to 2022. Results: We evaluated 100 cases of gastric cancer in this study. They had a mean age of 62.4 ± 13.44 years old (range 28 - 84 years) and were mostly men (n = 66, 66 %). On histopathologic evaluation, tubular carcinoma was the most common type (n = 45, 45%). We found a statistically significant correlation between the histologic type and perineural invasion (P-value = 0.024), lymphovascular invasion (P-value < 0.001), tumoral involvement of surgical margin (P-value = 0.012 ), infiltration depth of the primary tumor (pT) (P-value = 0.049) , number of metastatic lymph nodes (pN) (P-value = < 0.001) , tumor location in the antrum (P-value=0.033) and body (P-value = 0.013) , and tumor size (P-value = 0.002 and P-value = 0.031 in small and large size groups respectively). Conclusions: According to the findings, histologic type of gastric cancer correlates with perineural invasion, lymphovascular invasion, tumoral involvement of the surgical margin, pT, pN, and tumor location and size.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".