Declining Colectomy Rates for Nonmalignant Colorectal Polyps in a Large, Ethnically Diverse, Community-Based Population
Bibliographic record
Abstract
INTRODUCTION: Despite studies showing improved safety, efficacy, and cost-effectiveness of endoscopic resection for nonmalignant colorectal polyps, colectomy rates for nonmalignant colorectal polyps have been increasing in the United States and Europe. Given this alarming trend, we aimed to investigate whether colectomy rates for nonmalignant colorectal polyps are increasing or declining in a large, integrated, community-based healthcare system with access to advanced endoscopic resection procedures. METHODS: We identified all individuals aged 50-85 years who underwent a colonoscopy between 2008 and 2018 and were diagnosed with a nonmalignant colorectal polyp(s) at the Kaiser Permanente Northern California integrated healthcare system. Among these individuals, we identified those who underwent a colectomy for nonmalignant colorectal polyps within 12 months after the colonoscopy. We calculated annual colectomy rates for nonmalignant colorectal polyps and stratified rates by age, sex, and race and ethnicity. Changes in rates over time were tested by the Cochran-Armitage test for a linear trend. RESULTS: Among 229,730 patients who were diagnosed with nonmalignant colorectal polyps between 2008 and 2018, 1,611 patients underwent a colectomy. Colectomy rates for nonmalignant colorectal polyps decreased significantly from 125 per 10,000 patients with nonmalignant polyps in 2008 to 12 per 10,000 patients with nonmalignant polyps in 2018 (P < 0.001 for trend). When stratified by age, sex, and race and ethnicity, colectomy rates for nonmalignant colorectal polyps also significantly declined from 2008 to 2018. DISCUSSION: In a large, ethnically diverse, community-based population in the United States, we found that colectomy rates for nonmalignant colorectal polyps declined significantly over the past decade likely because of the establishment of advanced endoscopy centers, improved care coordination, and an organized colorectal cancer screening program.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".