Crewmember demographic factors and their association with brain and ocular changes following spaceflight
Bibliographic record
Abstract
More people are traveling to space for longer durations than ever before. Many long-duration flyers exhibit signs of Spaceflight Associated Neuro-ocular Syndrome (SANS). A greater understanding of the mechanisms and predictors of SANS may lead to new, more individually tailored countermeasures. Our objective here was to determine whether brain and ocular changes with spaceflight are related to each other and/or to crewmember demographic factors, including sex, age, body mass index, and prior spaceflight experience. We assessed brain change and ocular change associations and predictive models of changes in a cohort study. Our samples included 30 crewmembers with brain MRI but not ocular metrics, 37 with ocular but not brain MRI, and 22 with both sets of data. Approximately 25% of participants in each of these samples were female. Females showed greater free water reduction around the vertex of the brain from pre- to postflight than males. While not statistically significant, the odds ratio of males developing signs of SANS was approximately three times higher than for females. Unlike in past smaller studies, we found no association between brain changes and the development of signs of SANS. Interpretation of these findings should be tempered by the fact that our sample included a relatively small number of females.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".