Adaptation to motion sickness from torso rotation affects symptoms from supine head nodding.
Bibliographic record
Abstract
INTRODUCTION: Space motion sickness affects more than 50% of astronauts. Adaptation to one kind of nauseogenic motion can affect the response to another. We hypothesized that repeated exposure to torso rotation (TR) would diminish motion sickness (MS) due to supine head nodding (HN), a ground-based analogue for space sickness. METHODS: During TR, standing subjects swept their gaze back and forth between two targets located 135 degrees to either side of straight ahead. For HN, the subject lay supine on a platform with his/her head and neck hanging over the edge. The head was pitched back and forth between the horizontal position and rotated back as far as possible. MS was quantified in 5 male and 20 female subjects using numerical estimates of discomfort and a more detailed questionnaire. Susceptibility to TR and HN was determined on three occasions for each stimulus. At least 1 mo separated any two tests. Subjects were exposed to TR once a day for 7 consecutive days 1 mo after the final control session. Within 24 h of the seventh session, and 1 mo later, susceptibility to HN was evaluated again. RESULTS: There was a significant decrease in MS susceptibility during repeated exposure to TR (time to stop 17.7 min on Day 1, 31.8 min on Day 7). In contrast, there was a significant increase in MS susceptibility to HN after repeated exposure to TR (mean time to stop 11.7 min before TR, 9.5 min after). Susceptibility to HN was back to normal (time to stop 11.4 min) 1 mo later. DISCUSSION: Subjects can adapt to TR by repeated exposure. At the same time, susceptibility to HN increases. Strategies for pre-adaptation to space motion sickness need to be chosen carefully as they could actually be counterproductive.
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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 | 0.001 |
| 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 | 0.000 | 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".