Profiles and diagnostic patterns of colorectal cancers among 602 patients attending a tertiary care center in Saudi Arabia for suspected colic disease
Bibliographic record
Abstract
OBJECTIVES: Owing to the peaking rates and the multifaceted nature of colorectal cancers (CRC), understanding regional epidemiology and clinicopathological characteristics is critical. However, for the severe paucity in high-quality region-specific data, we aimed to determine the prevalence, distribution across age-gender, and identify common gastrointestinal pathological-features, and associations between patients' clinical-histories, definitive-diagnostic findings, and demographic-characteristics. METHODS: We retrospectively examined records of 602 patients (January 2020-December 2024). Data included demographics, diagnoses (e.g. adenocarcinoma, tubulovillous adenoma with dysplasia [TVA], dysplasia, chronic active colitis, no malignancy, polyps, or differential diagnosis), biopsy-type, and relevant clinical history. Descriptive statistics and inferential tests such as Chi-square and Mann-Whitney U were used. RESULTS: = 73 vs. 59) compared to females. Rectosigmoid-biopsy was the most common method for diagnosing adenocarcinoma (42.4%), whereas colon-biopsy was more frequent for TVA (27.1%). The highest proportion of "No malignancy" diagnosis was in the 15-29 years age group (males: 65.4%, females: 64.7%). Clinical histories indicating "colon-tumor or polyps" were strongly associated with adenocarcinoma (47.0%) and TVA (30.3%). Significant associations were found between diagnosis and specimen-type and clinical-history and diagnosis. CONCLUSIONS: This study highlights distinct age- and-gender-specific CRCs-patterns; particularly, prevalence of neoplastic-conditions in elderly with more frequent adenocarcinomas in males and variance of biopsy-sites by diagnosis. These support targeted-screening where more effective diagnostic and early treatment strategies are imperative in-line with the country's 2030-vision and UN's-Sustainable Development Goals-3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".