EP079 Feasibility of regional anesthesia in microgravity: a proof-of-concept study
Bibliographic record
Abstract
Please confirm that an ethics committee approval has been applied for or granted: Not relevant Background and Aims The ambitious goals of crewed deep space missions, like Nasa’s Artemis program and SpaceX’s colonization targets, require preparations for potential astronaut health crises. Innovative solutions are necessary to overcome the challenges of administering anesthesia in the unique environment of space and the physiologic changes associated with prolonged microgravity exposure. Regional anesthesia offers a viable solution to these challenges, but its feasibility is yet to be tested. Methods Our study assessed the feasibility of single-shot peripheral nerve blocks in a simulated microgravity environment (free-floating underwater) using a meat model. We randomized forty meat models to be injected under simulated microgravity and normal Earth gravity conditions. Post-injection, blinded assessors determined success rates. Assessed parameters included, ‘time to block’, ease of needle placement, and ease of image acquisition. Results Block success rates were comparable in both scenarios (80% normal gravity versus 85% microgravity, p > 0.999) and there was no difference in the rate of accidental intra-neural injections (5% versus 5%). The median time to block on land was 27 [IQR 21-69] seconds versus 35 [IQR 22-48] seconds in simulated microgravity (p = 0.751). Ease of needle placement and ease of image acquisition were similar in both conditions. Conclusions Despite challenges, regional anesthesia appears to be feasible in simulated microgravity. While our model is not a perfect analogue to true space conditions, it provides a foundation for subsequent research into the provision of anesthesia and analgesia during crewed space missions.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".