2848 The Impact of Multitarget Stool DNA (Cologuard) Testing in a Real World Population
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer (CRC) remains the second leading cause of death for carcinomas. Effective screening for CRC is paramount to prevent morbidity and mortality. Several options for screening alternatives exist with the newest, as of 2014, being the recommendation for multitarget stool DNA testing. A cross-sectional study at 90 sites, including private and academic centers, throughout the US and Canada evaluated average-risk patients utilizing stool specimens and follow-up screening colonoscopy. This 2014 study found that sensitivity for stool DNA testing of detecting colorectal cancer was 92.3%, advanced precancerous lesions 42.4%, and high-grade dysplasia 69.2%. METHODS: We collected real-world prospective data from January 31, 2017 through May 31, 2019 of 157 patients who had positive stool DNA testing and documented their colonoscopy findings. The patients all underwent colonoscopy at the same outpatient endoscopy center which has four board certified gastroenterologists, all more than 10 years out of training. Patients were all average-risk prior to receiving stool DNA screening. RESULTS: The mean age of patients was 68.8 years, with 99 female patients and 66 male patients. Three patients were found to have invasive adenocarcinoma which included the cecum, sigmoid and rectum. A total of 81 patients had no pathology or hyperplastic polyps and 73 patients had polyps that were either tubular adenomas or sessile serrated adenomas. CONCLUSION: In our study, 48% of patients had a positive predictive value (PPV) indicating those who had stool DNA testing followed by positive initial screening colonoscopies. We compared our data to the initial 2014 clinical trial by Imperiale et al with a 41.37% PPV. Interestingly, the number needed to treat in the clinical trial found that 166 individuals would have to undergo multitarget DNA testing for any colorectal cancer to be found and 31 for advanced precancerous lesions. Our real-world study determined that it took about 52 patients (3 in 157) to identify colorectal cancer and 1 in 2.15 patients for advanced precancerous lesions. Our study highlights the continued necessity of colonoscopies as the first line therapy for screening, even among average risk populations.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".