Anesthetic Gases: Environmental Impacts and Mitigation Strategies for Fluranes and Nitrous Oxide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anesthetic gases represent a small but significant portion of the environmental impact of health care in many countries. These compounds include several fluorocarbons commonly referred to as “fluranes”. The fluranes are greenhouse gases (GHG) with global warming potentials in the hundreds to thousands and are also PFAS compounds (per- and polyfluorinated alkyl substances) according to at least one definition. Nitrous oxide (N2O) is sometimes used as an adjunct in anesthesia, or for sedation, but has a significant stratospheric ozone depletion potential as well as GHG effects. Reducing emissions of these compounds into the environment is, therefore, a growing priority in the health care sector. Elimination or substitution of the highest impact fluranes with alternatives has been pursued with some success but limitations remain. Several emission control strategies have been developed for fluranes including adsorption onto solids, which has shown commercial promise. Catalytic decomposition methods have been pursued for N2O emission control, although mixtures of fluranes and N2O are potentially problematic for this technology. All such emission control technologies require the effective scavenging and containment of the anesthetics during use, but the limited available information suggests that fugitive emissions into the operating room may be a significant route for unmitigated losses of approximately 50% of the used fluranes into the environment. A better understanding and quantification of such fugitive emissions is needed to help minimize these releases. Further cost–benefit and techno-economic analyses are also needed to identify strategies and best practices for the future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it