Feasibility of ultrasound-guided nerve blocks in simulated microgravity: a proof-of-concept study for regional anaesthesia during deep space missions
Bibliographic record
Abstract
BACKGROUND: With crewed deep space exploration on the horizon, preparation for potential astronaut health crises in space missions has become vital. Administration of anaesthesia and analgesia presents many challenges owing to constraints specific to space (physiologic and ergonomic challenges associated with microgravity) and nonspecific factors (isolation and lack of supplies). Regional anaesthesia can be the safest option; however, we hypothesised that the ergonomics of microgravity would compromise ease and accuracy of nerve blocks. METHODS: We evaluated the feasibility of regional anaesthesia in a simulated microgravity environment (free-floating underwater conditions) using a meat (bovine muscle) model. Forty meat models were randomised for injection under simulated microgravity or normal gravity conditions. Success rates were determined by blinded assessors after injection. Parameters assessed included time to block, ease of image acquisition, and ease of needle placement. RESULTS: The median time to block in normal gravity was 27 (interquartile range 21-69) s vs 35 (interquartile range 22-48) s in simulated microgravity (P=0.751). Ease of image acquisition was similar in both conditions, as was ease of needle placement. There was no significant difference in the rate of accidental intraneural injections (5% vs 5%), with block success rates comparable in both scenarios (80% normal gravity vs 85% microgravity, P>0.999). CONCLUSIONS: Regional anaesthesia appears feasible for experts in simulated microgravity despite the ergonomic challenges. Although our model has limitations and might not fully capture the complexities of actual space conditions, it provides a foundation for future research into anaesthesia and analgesia during deep space missions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".