Colorectal Cancer in the Kingdom of Saudi Arabia: Need for Screening
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Colorectal cancer (CRC) is a major health problem in the Kingdom of Saudi Arabia (KSA). Our aim was to characterize the epidemiology of CRC in the Saudi population. DESIGN AND SETTING: Retrospective analysis of all cases of CRC recorded in the Saudi Cancer Registry (SCR) between January 2001 and December 2006 amongst Saudi citizens in KSA. PATIENTS AND METHODS: Data were retrieved from the database of the SCR. Descriptive statistics was performed using SPSS. RESULTS: A total of 4,201 cases of CRC were registered in the SCR. The incidence of CRC increased between 2001 and 2006. The mean age of patients at the time of diagnosis was 58 years; most patients were above 45 years of age (n=3322; 79.1%). At the time of diagnosis, 977 patients (23.0%) presented with localized disease and 1,018 (24.0%) had distant metastasis. The most frequent pathological variant was adenocarcinoma (73%), with grade 2 (moderately differentiated) being the most common grade among all variants (61%). For all cancer grades, the frequency of CRC was significantly higher among patients >45 years (P=0.004), who presented with more advanced disease (stages III and IV) (P=0.012). Based on logistic regression, age >45 years was associated with advanced regional presentation (P=0.001). Tumor grade was associated with advanced regional presentation and metastasis. CONCLUSION: There was an increase in the incidence of CRC between 2001 and 2006. The age at the time of diagnosis was low when compared with reports from developed countries. A nationwide approach is needed to encourage and illustrate the importance of screening programs.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".