Volatile gas scavenging in the paediatric intensive care unit: Occupational health and safety assessment
Bibliographic record
Abstract
Introduction: The use of volatile anesthetic agents in the paediatric intensive care unit (PICU) is experiencing increased interest since the availability of the miniature vapourizing device. However, the effectiveness of scavenging systems in the presence of humidifiers in the ventilator circuit is unknown. Approach Methods: We performed a bench study to evaluate the effectiveness of the Deltasorb® scavenging system in the presence of isoflurane and active humidity by simulating both infant and child ventilator test settings. A total of four ventilators were set to ventilate test lungs, all with active humidity and a Deltasorb scavenging canister collecting exhaled ventilation gas. Two ventilators also had isoflurane delivered using the Anesthesia Conserving Device- small (ACD®-S) on the inspiratory limb (also called alternative ventilator configuration). We performed instantaneous measurements of isoflurane and continuous sampling with passive badges to measure average environmental exposure over a test period of 6.5 hours. Scavenging canisters were returned to the company, where desorption analysis showed the volume of water and isoflurane captured in each canister. Findings: Both instantaneous point sampling and diffusive sampling results were below the occupational exposure limit confirming safety. The canisters collected both isoflurane and a portion of the water vapour delivered; the percentage of captured water and isoflurane collected in infants was higher than the child ventilator test settings. Practice implications Conclusion: The tested scavenging configuration was effective in maintaining a safe working environment with active humidity and inspiratory limb (alternative) ventilator configuration of the the miniature vapourizing device.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".