Determination of Effective Factors on Survival of GI Cancers: Results of Five Years Follow up in Iranian Population
Bibliographic record
Abstract
BACKGROUND: The gastrointestinal cancers are among the most common cause of cancer-related death and their long term survival is very low. This study was aimed to determine the effective factors on survival of gastrointestinal cancers among Iranian population during 5 years of follow up. METHODS: In total, 157 patients diagnosed as gastrointestinal cancers from 2007 to 2009 in the only center of endoscopy in Alvand city, northwest of Qazvin province were included and followed for five years. The univariate and multivariate analysis were done using Kaplan-Meier method and the Cox model respectively. RESULTS: Observations of 146 patients were analyzed (99 (67.8%) males and 47 (32.2%) females). The mean age was 64.73± 13.23 and 58.28±13.91 for females and males respectively. The one and three years survival rates for esophageal cancer were 28% and 9% and the one, three and five years survival rates for gastric cancer were 31%, 26% and 14% and for colorectal cancer were 96%, 86% and 75% respectively. In the univariate analysis, variables of age, educational level, ethnicity, smoking, type of cancer, stage of disease and type of treatment had significant effects on survival. In the multivariate analysis, the type of cancer and type of treatment affected the survival of patients as effective factors (p<005). CONCLUSION: Patients with esophageal cancer and those who underwent RT &/or CT are exposed to higher risk of death. Combination therapies (Surgery and adjuvant or neoadjuvant therapy) were related to be her survival. Early diagnosis and use of extended cancer screening programs seem necessary to improve survival.
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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 | 0.001 |
| 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".