Bibliographic record
Abstract
Colorectal cancer (CRC) is the third most common type of cancer diagnosed in Canada, and is the leading cause of cancer-related deaths in nonsmokers. Although CRC is considered to be 90% curable if detected early, the majority of patients present with advanced stage III or IV disease. An effective screening test may significantly decrease disease burden. The present paper examines the rationale and potential of fecal DNA testing as an alternative and adjunct to other CRC screening tests. The most efficacious fecal DNA test developed to date has a sensitivity and specificity of 87.5% and 82%, respectively. The approach has a higher positive predictive value than the currently used fecal occult blood test and offers a noninvasive option to patients. It is not reliant on the presence of bleeding, which may be intermittent or altogether absent. The test is now commercially available and is supported by a number of American insurers. Current challenges include cost reduction and demonstration of mortality benefit in a rigorous clinical trial. Despite current challenges, fecal DNA testing is worth pursuing. Both the American Gastroenterological Society and the American Cancer Society maintain that molecular testing is in its infancy but is promising. Fecal DNA testing has the potential to be an exciting addition to the current armament of CRC screening options.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".