NATIONAL SCREENING PROGRAM FOR COLORECTAL CANCER
Bibliographic record
Abstract
Colorectal Cancer:Colorectal cancer is one of the preventable cancers in humans. From a simple polyp to cancer, it is a long journey and gives us a window of opportunity to intervene and prevent it. Historically, Pakistan has been considered a low-prevalence country for colorectal cancer but changing epidemiological patterns dictate that we should think of implementing bowel screening programs for early detection and risk reduction. More than 1.9 million new colorectal cancer (including anus) cases and 935,000 deaths were estimated to occur in 2020, representing about one in 10 cancer cases and deaths. (Globocan 2020). Colorectal Cancer is the 3rd most common cancer among men and 2nd most common cancer among women, worldwide. (1). CRC mortality rates have been declining in the USA and Canada, whereas in many countries like Latin America and the Caribbean (LAC), the mortality rates are increasing. This difference between Canada and the US with the rest of the countries in the Americas serves as an indication of differences that may exist in health care, including CRC screening, early detection, and treatment. There are perhaps lessons that can be learned from the USA and Canada experiences with CRC programs that can be used to address the growing burden of CRC in LAC
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".