Motion sickness: current concepts and management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Motion sickness is an ancient phenomenon that affects many people. Nausea, vomiting, disorientation, sweating, fatigue, and headache are just few of the many signs and symptoms that are commonly experienced during an episode of motion sickness. In the present review, we will provide an overview of the current research trends and topics in the domain of motion sickness, including theoretical considerations, physiological and neural mechanisms, individual risk factors, and treatment options, as well as recommendations for future research directions. RECENT FINDINGS: More recently, motion sickness has been in the focus of attention in the context of two global technological trends, namely automated vehicles and virtual reality. Both technologies bear the potential to revolutionize our daily lives in many ways; however, motion sickness is considered a serious concern that threatens their success and acceptance. The majority of recent research on motion sickness focuses on one of these two areas. SUMMARY: Aside from medication (e.g. antimuscarinics, antihistamines), habituation remains the most effective nonpharmacological method to reduce motion sickness. A variety of novel techniques has been investigated with promising results, but an efficient method to reliably prevent or minimize motion sickness has yet to emerge.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it