Four years Incidence Rate of Colorectal Cancer in Iran: A Survey of National Cancer Registry Data - Implications for Screening
Bibliographic record
Abstract
INTRODUCTION: Design and implementation of screening programs in each country must be based on epidemiological data. Despite the relatively high incidence of CRC, there is no nationwide comprehensive program for screening in Iran. This study was designed to investigate national CRC data and help to determine guidelines for screening. METHODS: Incidence data used in this study were obtained from Iranian annual of National Cancer Registration report. Age standardized rates (ASR)were calculated using world standard population and were categorized by age, sex, anatomic subsite and morphology of tumor. Data were analyzed using SPSS.V.13 and Open Source Epidemiologic Statistics for Public Health software (OpenEpi v.2.3.1). RESULTS: A quarter of cases were less than 50 years of age. The majority of tumors were detected in the colon. The overall ASR in the four years period was 38.0 per 100000 and was higher for men compared women (P<0.05). Incidence rate of colorectal cancer increased with age. CONCLUSION: Results of present study indicated that incidence of colorectal cancer is relatively high in Iran. Incidence of CRC in people under 50 years and in rectum were reported higher than other countries that related etiologic factors should be investigate in further studies. According to the increasing of ASR after age 50 years, it seems that onset of screening at age 50 would be appropriate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".