Air suctioning during colon biopsy forceps removal reduces bacterial air contamination in the endoscopy suite
Bibliographic record
Abstract
BACKGROUND AND STUDY AIMS: Bacterial contamination of endoscopy suites is of concern; however studies evaluating bacterial aerosols are lacking. We aimed to determine the effectiveness of air suctioning during removal of biopsy forceps in reducing bacterial air contamination. PATIENTS AND METHODS: This was a prospective single-blinded trial involving 50 patients who were undergoing elective nontherapeutic colonoscopy. During colonoscopy, endoscopists removed the biopsy forceps first without and then with suctioning following contact with the sigmoid mucosa. A total of 50 L of air was collected continuously for 30 seconds at 30-cm distance from the biopsy channel valve of the colonoscope, with time starting at forceps removal. Airborne bacteria were collected by an impactor air sampler (MAS-100). Standard Petri dishes with CNA blood agar were used to culture Gram-positive bacteria. Main outcome measure was the bacterial load in endoscopy room air. RESULTS: At the beginning and end of the daily colonoscopy program, the median (and interquartile [IQR] range) bioaerosol burden was 4 colony forming units (CFU)/m (3) (IQR 3 - 6) and 16 CFU/m (3) (IQR 13 - 18), respectively. Air suctioning during removal of the biopsy forceps reduced the bioaerosol burden from a median of 14 CFU/m (3) (IQR 11 - 29) to a median of 7 CFU/m (3) (IQR 4 - 16) ( P = 0.0001). Predominantly enterococci were identified on the agar plates. CONCLUSION: The bacterial aerosol burden during handling of biopsy forceps can be reduced by applying air suction while removing the forceps. This simple method may reduce transmission of infectious agents during gastrointestinal endoscopies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".