S1023 Colonoscopy Performed Outside of Hospital in Ontario: Trends, Patient and Endoscopist Factors
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is the second most frequent cancer and the second cause of mortality related to cancer in Argentina.The detection of premalignant lesions (PML) through colonoscopy has been demonstrated to be effective in the prevention of CRC.It is important to be aware of the prevalence of these lesions to estimate their impact on the prevention of CRC in our population.The aim of this study was to assess the prevalence of PML in asymptomatic subjects with different levels of risk of CRC.METHODS: A cross-sectional analysis was performed based on the colonoscopy records of asymptomatic subjects screened for CRC in a General Hospital of Buenos Aires City, Argentina, between July 2004 and March 2007.The prevalence of PML was assessed in 2 groups: 1) average risk and 2) first-degree relatives with CRC.Patients with incomplete colonoscopy, poor preparation or second-degree relatives with CRC were excluded.PML was defined as polyps or flat adenomas with low or high-grade dysplasia.Multivariate logistic analysis was performed to establish the association between individual characteristics and PML.RESULTS: Data from 1233 subjects was collected (female= 733, mean age= 67.6 years SD= ±11.2).We found a global prevalence of 24.1% for PML and 1.1% for CRC.Average risk group (n=476; female= 246; mean age=61.8years SD= ±9,06) showed a prevalence of 22.1% for PML , in which 19.2% had low-grade and 2.5% high-grade dysplasia.The prevalence of CRC was 1.05%.First-degree relatives group (n=757; female =487; mean age= 54.9 years SD= ±11.53) showed a prevalence of 25.2% (CI 95%=22.1-28.3), in which 20% had lowgrade and 4.8% high-grade dysplasia.The prevalence of CRC in this group was 1.2%.In the validation set a logistic regression model showed that family history of CRC was independently associated with increased risk of PML (odds ratio=1.54;CI 95%= 1.15-2.05;P=0.004).This difference was greater in younger subjects (table 1).Male sex was also independently associated with an increased risk of PML (odds ratio=1.50;IC 95%= 1.15-1.97;P=0.003).CONCLUSION: Approximately a quarter of this population presented at least one PML and a significantly increased risk in first-degree relatives with CRC and male sex was observed.These results may contribute to issue local guidelines for CRC prevention.Table 1.Prevalence in different age groups
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".