Adenoma Detection Rate is Dependent on the Colonoscopy Case Volume and Experiences of the Provider
Bibliographic record
Abstract
Introduction: Colonoscopy quality measurement using adenoma detection rate (ADR) is now a standard practice. Various reports suggesting withdraw time, bowel preparation quality, gender, age, ethnicity, and cap devices have impact on ADR. At our institution, we have systematically collected and analyzed 12720 colonoscopy reports from 2011 to 2017. By plotting the ADR for each quarter per provider over the years, we found a pattern of relationship between ADR, volume of cases performed, and years of practice after fellowship training. Methods: Between 2011 and 2017, we have reviewed every single colonoscopy report and associated pathology report performed at Mayo Clinic Health System in Mankato, MN. A dedicated staff member was responsible for combining the data and for entry into a spreadsheet. Variables including provider, withdraw time, polyp detection, tissue type of polyp, location of polyp, size of polyp, indication, sex, age were recorded. The data collection and graphs were obtained using Microsoft Excel. The statistical analysis were performed using R and Matlab. Results: When we plotted the ADR per quarter over the years between the providers and the team averages, a distinct pattern of relationship has emerged. The ADR for each new provider in our practice improves over time. The ADR reached a plateau between 60% and 70% after a provider joined our practice for more than 2 years (Figure #1). When we further plotted the ADR against the number of cases performed per provider during this period, two distinct groups of providers have emerged (Figure #2). The group with case volume above a thousand has average ADR above 60%. The group less than a thousand case volume had average ADR below 50% during this period (P=0.001496).Figure: ADR for Each Provider.Figure: ADR between High Volume vs Low Volume Providers.Conclusion: The quality of colonoscopy is similar to complication rate of any procedure in medical practices, the high volume provider has better outcome and lower complication rate. The quality depends on the case volume and experiences of the provider. After 2 to 3 years post-fellowship, the ADR of our new providers reached a plateau. At our institution, the ADR plateau is above 60%. The plateau is reached after our new provider has performed more than a thousand colonoscopy cases in total. The high volume colonoscopy providers have significantly higher ADR.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".