Determining Risk Factors for Gastric and Esophageal Cancers between 2009-2015 in East-Azarbayjan, Iran Using Parametric Survival Models
Bibliographic record
Abstract
Background: Esophageal cancer (EC) and Gastric cancer (GC) have been identified as two of the most common cancers in the northeastern regions of Iran. The increasing rates of these types of cancers requires attention. This study aims to assess the potential risk factors for these two cancers and then determine shared risk factors between them in a population of Iranian patients using parametric survival models. Methods: This retrospective cohort study was conducted using 127 patients with EC and 184 patients with GC in East Azarbaijan, Iran who were diagnosed and registered during the years 2009-2010 in Iran’s National Cancer Control Registration Program and were followed for five years. Parametric survival models were used to find the risk factors of the patients. Akaike Information Criteria was used to identify the best parametric model in this study. Interaction analysis was used to determine shared risk factors between EC and GC. Results: The mean (±standard deviation) age of diagnoses for EC and GC were 66.92(±11.95) and 66.5(±11.5) respectively. The survival time ranges of GC patients was (0.07-70.33) and the survival time ranges were from 0.10 to 69.03 months for EC patients. Multivariable Log- logistic model showed that being married (OR=2.25, 95% CI: 1.33 - 3.81) for EC patients and Esophagectomy surgery for EC (OR: 1.62, 95% CI: 1.04 – 2.55) and GC (OR: 1.60, 95% CI: 1.02 – 2.53) had significant effects on survival. Age at the time of diagnosis, job status, and Esophagectomy surgery were statistically comparable regarding their magnitude of effect on survival of two cancers (all Ps>0.05). Conclusion: Esophagectomy surgery and being married were important risk factors in EC and GC. The log-logistic model was the most appropriate statistical approach to identify significant risk factors on survival of both cancers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".