NATIONAL SCREENING PROGRAM FOR COLORECTAL CANCER
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it