Is There a Difference in Adenoma Detection Rates According to Indication? An Experience in a Panamanian Colorectal Cancer Screening Program
Bibliographic record
Abstract
Background: The benefit of colorectal cancer screening in reducing cancer risk and related death is unclear. There are quality measure indicators and multiple factors that affect the performance of a successful colonoscopy. The main objective of our study was to identify if there is a difference in polyp detection rate (PDR) and adenoma detection rate (ADR) according to colonoscopy indication and which factors might be associated. Methods: We conducted a retrospective review of all colonoscopies performed between January 2018 and January 2019, in a tertiary endoscopic center. All patients ≥ 50 years old scheduled for a nonurgent colonoscopy and screening colonoscopy were included. We stratified the total number of colonoscopies into two categories according to the indication: screening vs. non-screening, and then calculated PDR, ADR and serrated polyp detection rate (SDR). We also performed logistic regression model to identify factors associated with detecting polyps and adenomatous polyps. Results: A total of 1,129 and 365 colonoscopies were performed in the non-screening and screening group, respectively. In comparison with the screening group, PDR and ADR were lower for the non-screening group (33% vs. 25%; P = 0.005 and 17% vs. 13%; P = 0.005). SDR was non-significantly lower in the non-screening group when compared with the screening group (11% vs. 9%; P = 0.53 and 22% vs. 13%; P = 0.007). Conclusion: In conclusion, this observational study reported differences in PDR and ADR depending on screening and non-screening indication. These differences could be related to factors related to the endoscopist, time slot allotted for colonoscopy, population background, and external factors.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".