Defining standards for fluoroscopy in gastrointestinal endoscopy using Delphi methodology
Bibliographic record
Abstract
Abstract Background and study aims Use of fluoroscopy in gastrointestinal endoscopy is an essential aid in advanced endoscopic interventions. However, it also raises concerns about radiation exposure. This study aimed to develop consensus-based statements for safe and effective use of fluoroscopy in gastrointestinal endoscopy, prioritizing the safety and well-being of healthcare workers and patients. Methods A modified Delphi approach was employed to achieve consensus over three rounds of surveys. Proposed statements were generated in Round 1. In the second round, panelists rated potential statements on a 5-point scale, with consensus defined as ≥80% agreement. Statements were subsequently prioritized in Round 3, using a 1 (lowest priority) to 10 (highest priority) scale. Results Forty-six experts participated, consisting of 34 therapeutic endoscopists and 12 endoscopy nurses from six continents, with an overall 45.6% female representation (n = 21). Forty-three item statements were generated in the first round. Of these, 31 statements achieved consensus after the second round. These statements were categorized into General Considerations (n = 6), Education (n = 10), Pregnancy (n = 4), Family Planning (n = 2), Patient Safety (n = 4), and Staff Safety (n = 5). In the third round, accepted statements received mean priority scores ranging from 7.28 to 9.36, with 87.2% of statements rated as very high priority (mean score ≥ 9). Conclusions This study presents consensus-based statements for safe and effective use of fluoroscopy in gastrointestinal endoscopy, addressing the well-being of healthcare workers and patients. These consensus-based statements aim to mitigate risks associated with radiation exposure while maintaining benefits of fluoroscopy, ultimately promoting a culture of safety in healthcare settings.
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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 | 0.183 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".