S3089 Is There a Gender Bias in Stool FIT/DNA Testing?: Experience in Outpatient Endoscopy Unit in Michigan
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer remains the second leading cause of deaths from cancer in the United States of America. Multiple screening tests are available for early detection of cancer. Several previous studies have suggested that women are less likely to be screened for colorectal cancer compared to men. During the current COVID-19 crisis there was a significant drop in the number of people screened for colon cancer. The impact from delayed screening can have adverse outcomes in colorectal cancer patients. We reviewed our data of our referrals for positive stool FIT-DNA testing before the COVID-19 crisis. METHODS: We prospectively reviewed all cases referred for a positive stool FIT-DNA test to our outpatient endoscopy center from February 2018 to December 2018. A total of 110 patients were included in the study that included 74 females and 36 males. Any patient found not to be at an average risk for colon cancer prior to the stool FIT/DNA test were excluded(e.g. prior history of adenomatous colon polyp or family history of colon cancer). Each patient's findings were collected. All polyps found were recorded with measurements. Biopsies of all the polyps and masses were reviewed and tabulated. Polyps were classified as adenomatous polyp, serrated polyps and hyperplastic polyps. Note was made of any polyps that revealed high grade dysplasia. RESULTS: A surprising finding was noted of more female patients(74 versus 36) being referred for a positive stool FIT/DNA tests. 39 of the 110 patients had at least one of the the following findings- adenomatous polyp at least 10 mm in size, serrated polyp, adenomatous polyp of any size with high grade dysplasia or mass. CONCLUSION: Gender biases exist in colorectal cancer screening test picked by the patients. Although ours is a small study it suggests that females are more likely to accept stool FIT/DNA test. In the current COVID-19 crisis there is a significant hesitation from patients to agree for a screening colonoscopy. A delay in screening can have adverse outcomes in patients with colorectal cancer. A positive stool FIT/DNA test is likely to have patients agree to have a colonoscopy. Despite the false positive rates there is an opportunity to get several advanced adenomas and masses to be picked up earlier in the current COVID-19 crisis during which patients are reluctant to come for a screening colonoscopy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".