Ambient air pollution in gastrointestinal endoscopy unit; rationale and design of a prospective study
Bibliographic record
Abstract
BACKGROUND: A gastrointestinal endoscopy unit is frequently exposed to gastrointestinal gas expelled from patients and electrocoagulated tissue through carbonation for the treatment of gastrointestinal neoplasms or hemostasis of gastrointestinal bleeding. This can be potentially harmful to the health of not only the healthcare personnel but also patients who undergo endoscopic examinations. However, there has been scarce data on air quality in the endoscopy unit. This study aimed to measure the air quality in the gastrointestinal endoscopy unit. METHODS: This is a prospective study using conventional portable passive air quality monitoring sensors in the gastrointestinal endoscopy unit. We will check the 6 main indoor air quality indices, as well as the atmospheric temperature, pressure, and humidity in the endoscopy unit of a single hospital in Korea. These indices are as follows: carbon dioxide (CO2), total volatile organic compounds (VOCs), particulate matter that has a diameter of <2.5 μm, nitrogen dioxide (NO2), carbon monoxide (CO), and ozone. The indices will be checked in the endoscopy unit, including the procedural area, recovery area, and area for disinfection and cleansing of equipment, at 1-minute intervals for at least 1 week, and the type and number of endoscopic procedures will also be recorded. The primary outcome of this study is to determine whether the air quality indices exceed safety thresholds and whether there is any association between ambient air pollution and the type and number of endoscopic procedures. CONCLUSION: The results of this study will provide evidence for health-related protective strategies for medical practitioners and patients in the endoscopy unit.
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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 | 0.043 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".