Re-Educating Residents About Non-Invasive Colorectal Cancer Screening: An Approach to Improving Colon Cancer Screening Compliance
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer is the third leading cause of cancer death; therefore early detection by screening is beneficial. Residents at a clinic in NJ, USA were not offering other forms of colon cancer screening when patients refused colonoscopy, which lead to the creation of the quality improvement project. METHODS: Residents practicing at the clinic were given an anonymous survey determining which method of colon cancer screening they used and which alternative method they offered when patients refused the original method. The residents were educated about all methods of colon cancer screening and the residents were resurveyed. RESULTS: A total of 64% of residents offered less invasive testing when colonoscopy was refused. Six months after education, 95% of residents offered less invasive testing when colonoscopy was refused. CONCLUSIONS: Early detection and removal of polyps by colonoscopy reduce the risk of cancer development. Colonoscopy is the gold standard for colon cancer screening; however other less invasive modalities are approved. This quality improvement project lead to offering the fecal immunochemical test or fecal occult blood test once patients refused colonoscopy at the clinic, increasing the number of patients receiving colorectal cancer screening, and thus providing better medical care.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".