A260 AGREEMENT BETWEEN COLONOSCOPY-DETECTED AND PATHOLOGY-CONFIRMED COLORECTAL CANCER IN THE 2012 TIANJIN COLORECTAL CANCER SCREENING PROGRAM
Bibliographic record
Abstract
The Tianjin Colorectal Cancer (CRC) Screening Program uses a questionnaire (HRFQ) to determine risk status and the initial screening test: stool testing (FIT) for average-risk and colonoscopy for high-risk individuals. To determine agreement between colonoscopy-detected CRC and pathology-confirmed CRC. A retrospective cohort study was conducted using the data from the 2012 Tianjin CRC Screening Program in Tianjin, China. Participants were aged 60 to 74 and residents of Tianjin who completed both the HRFQ and colonoscopy. Demographics and clinical data were obtained as well as FIT, colonoscopy and pathology results. Cohen’s Kappa was used to determine agreement between colonoscopy-detected and pathology-confirmed CRC. In 2012, 19,096 individuals completed the HRFQ and colonoscopy, of which 10,907 (57.1%) were HRFQ positive and 7,986 (41.8%) were colonoscopy positive. Colonoscopy positive findings included 7160 polyps, 728 adenomas and 102 CRC. Of all colonoscopy positive findings, 1,256 (15.7%) were sent to pathology where 326 were confirmed CRC. Only 59 (18.1%) of the pathology-confirmed CRC were detected at colonoscopy. Agreement between CRC detected at colonoscopy and CRC confirmed by pathology was Kappa=0.22 (95%CI=0.16–0.27). Poor agreement was found between colonoscopy-detected and pathology-confirmed CRC, suggesting that many CRCs are undetected and untreated. Effectiveness of the screening program would be improved by sending all removed tissues to pathology. Comparison of colonoscopy-detected and pathology-confirmed colorectal cancer (CRC) in the 2012 Tianjin CRC Screening Program Fonds de recherche du Québec- Santé
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".