a Korean Population: A Small Step for Gastroenterologists but One Giant Leap for Koreans
Bibliographic record
Abstract
cancer may correctly be assessed by conducting a population-based study, and the prevalence of interval cancer based on a population study in the West was 4.0%−7.9%.4-7,9,10 Kim et al.8 also suggested that young age and right-side location were independent factors associated with interval cancer in a multivariate analysis. In a recent study from Can-ada, however, female sex, older age, and performance of the colonoscopy by a non-gastroenterologist were identified as predictors of interval cancers after a negative colonoscopy.3,4 Although other authors suggested the accelerated tumor bi-ology in young patients as a cause of interval cancer, the au-thors of the Canadian studies3,4 suggested that a deficiency in the quality of colonoscopic data rather than accelerated tumor biology was the cause of most of the interval cancers occurring after a negative colonoscopy. Furthermore, infor-
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".