Predictors of Colorectal Cancer After Negative Colonoscopy: A Population-Based Study
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
OBJECTIVES: A higher proportion of colorectal neoplasia among women occurs in the proximal colon, which might be more frequently missed by colonoscopy. There are no data on predictors of developing colorectal cancer (CRC) after a negative colonoscopy in usual clinical practice. We evaluated gender differences and predictors of CRC occurring after a negative colonoscopy. METHODS: All individuals 40 years or older with negative colonoscopy were identified from Manitoba's provincial physicians' billing claims database. Individuals with less than 5 years of coverage by the provincial health plan, earlier CRC, inflammatory bowel disease, resective colorectal surgery, or lower gastrointestinal endoscopy were excluded. CRC risk after negative colonoscopy was compared to that in the general population by standardized incidence ratios. Cox regression analysis was performed to determine the independent predictors of CRC occurring after negative colonoscopy. RESULTS: A total of 45,985 individuals (18,606 men; 27,379 women) were followed up for 229,090 person-years. After a negative colonoscopy, men had a 40-50% lower risk of CRC diagnosis through most of the follow-up time. Risk among women was similar to that of women in the general population in the first 3 years and then was 40-50% lower. Older subject age and performance of index colonoscopy by non-gastroenterologists were independent predictors for early/missed CRC (cancers occurring within 3 years of negative colonoscopy). CONCLUSIONS: Women may have a higher rate of missed/early CRCs after negative colonoscopy. Predictors of missed/early CRCs after negative colonoscopy include older age and performance of index colonoscopy by a non-gastroenterologist.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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