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Record W2999901333 · doi:10.7573/dic.2019-9-4

Motion sickness: an overview

2019· review· en· W2999901333 on OpenAlexaff
Alexander K. C. Leung, Kam‐Lun Ellis Hon

Bibliographic record

VenueDrugs in Context · 2019
Typereview
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsMotion sicknessPhysical medicine and rehabilitationMedicineNauseaMotion (physics)HabituationAudiologyPsychiatryComputer scienceArtificial intelligenceAnesthesia

Abstract

fetched live from OpenAlex

BACKGROUND: Motion sickness is a common phenomenon that affects almost everybody at some point in their lifetime. Clinicians should be familiar with the proper management of this condition. OBJECTIVE: To provide an update on the current understanding of the pathophysiology and management of motion sickness. METHODS: A PubMed search was performed with Clinical Queries using the key term 'motion sickness.' The search strategy included meta-analyses, randomized controlled trials, clinical trials, observational studies, and reviews. The search was restricted to English literature. The information retrieved from the earlier search was used in the compilation of the present article. RESULTS: for developing motion sickness is when the brain receives conflicting information from different sensors about real body movements or virtual environment. The principal sensors are the eyes, the vestibular apparatus, and proprioceptive receptors. The conflicting information is judged in relation to a pattern of expected associations formed under normal or experienced conditions stored in the brain. Motion sickness typically presents with malaise, anorexia, nausea, yawning, sighing, increased salivation, burping, headache, blurred vision, non-vertiginous dizziness, drowsiness, spatial disorientation, difficulty concentrating, and sometimes vomiting. Simple behavioral and environmental modifications can be effective in the prevention of motion sickness. Medications that are effective in the prophylaxis and/or treatment of motion sickness include anticholinergics, antihistamines, and sympathomimetics. CONCLUSION: In most cases, motion sickness can be prevented by behavioral and environmental modifications (avoidance, habituation, and minimization of motion stimuli). Pharmacotherapy should be considered in the prevention and/or treatment of more severe motion sickness and for patients who do not respond to conservative measures. Medications are most effective when combined with behavioral and environmental modifications. Drugs that are effective in the prophylaxis and/or treatment of motion sickness include anticholinergic agents and antihistamines.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.175
GPT teacher head0.405
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations106
Published2019
Admission routes1
Has abstractyes

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