Past, Present and Future? of Colorectal Cancer Epidemiology and Clinical Study in Misan
Bibliographic record
Abstract
Background: Colorectal carcinoma is commonest cancer of GIT. It is represent third cancer in man worldwide beyond lung and prostate cancers. It is fourth cancer in woman beyond breast, lung and uterus cancers. Deaths from colorectal cancer is more in compare with other GIT cancers. The study aimed to determine epidemiological and clinical data of colorectal cancer in Misan province.Methods: Our study conducted in Misan province, Iraq. The data were collected from 2013 to 2016. Seventy one patients that found have colorectal cancer. An epidemiological, clinical and descriptive study perform which included frequency of gender, age, residency, site of cancer, family history, past history, year of onset, smoking history, alcohol intake, presentation of cancer at time of diagnosis, staging and histopathology pattern in relation to colorectal cancer.Results: Overall prevalence of colon and rectum carcinoma is 3.75%. The most age group affected was 51-60 years as 30.99%. The gender and residency of patients have no effect on cancer percent. Obesity, Family history, cigarette smoking and alcohol consumption represented risk factors for colorectal cancer. In 42.25% of patients had family history of cancer. Most common site of colorectal carcinoma was left colon, which present in 61.97%. Conclusion: There was slight increase in new cases detection of colorectal carcinoma from 2013 to 2016. Advanced stages of colorectal cancer were most common stages description as stage IIIA, IIIB, IIIC and stage IV in 12.67%, 16.90%, 19.72% and 15.49% respectively. The common histopathological pattern of colorectal cancer was moderately differentiated adenocarcinoma as 53.52%.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".