Trauma sonography for use in microgravity.
Bibliographic record
Abstract
Sonography is the only medical imaging modality aboard the ISS, and is likely to remain the leading imaging modality in future human spaceflight programs. While trauma sonography (TS) has been well recognized for terrestrial trauma settings, the technique had to be evaluated for suitability in spaceflight prior to adopting it as an operational capability. The authors found the following four-phased evaluative approach applicable to this task: 1) identifying standard or novel terrestrial techniques for potential use in space medicine; 2) developing and testing these techniques with suggested modifications on the ground (1 G) either in clinical settings or in animal models, as appropriate; 3) evaluating and refining the techniques in parabolic flight (0 G); and 4) validating and implementing for clinical use in space. In Phase I of the TS project, expert opinion and literature review suggested TS to be a potential screening tool for trauma in space. In Phase II, animal models were developed and tested in ground studies, and clinical studies were carried out in collaborating trauma centers. In Phase III, animal models were flight-tested in the NASA KC-135 Reduced Gravity Laboratory. Preliminary results of the first three phases demonstrated the potential clinical utility of TS in microgravity. Phase IV studies have begun to address crew training issues, onboard imaging protocols, and data transfer procedures necessary to offer the modified TS technique for space use.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".