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Treatment for Vestibular Disorders

2016· article· en· W2440392402 on OpenAlexaff
Courtney D. Hall, Susan J. Herdman, Susan L. Whitney, Stephen P. Cass, Richard A. Clendaniel, Terry D. Fife, Joseph M. Furman, Thomas S.D. Getchius, Joel A. Goebel, Neil T. Shepard, Sheelah N. Woodhouse

Bibliographic record

VenueJournal of Neurologic Physical Therapy · 2016
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsCARE Canada
Fundersnot available
KeywordsVestibular disordersVestibular systemMedicineAudiologyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Dizziness is very common, but it is never normal. Dizziness can make performing daily activities, work, and walking difficult. Many people get dizzy when they turn their head, which can cause problems with walking and makes people more likely to fall. Most of the time dizziness is not from a life-threatening disease. Often dizziness is because of a disorder of the vestibular (or inner ear balance) system. People can get vestibular disorders from infections in the ear, problems with the immune system, medications that harm the inner ear, and rarely from diabetes or stroke because of a lack of blood flow to the inner ear. Stress, poor sleep, migraines, overdoing some activities, and feeling sad can increase symptoms. New guidelines for the treatment of vestibular disorders were published in the April 2016 issue of the Journal of Neurologic Physical Therapy. The guideline describes which exercises are best to treat the dizziness and balance problems commonly seen with an inner ear disorder. NEW INSIGHTS Early treatment may help people to get better faster. Treatments focus on eye and head exercises plus balance exercises to help relieve dizziness and improve the ability to walk without losing balance. A physical therapist will tailor these treatments to your specific problems on the basis of a complete examination. PRACTICAL ADVICE For people with inner ear dizziness, their dizziness may get better even without treatment. For some people, their dizziness does not get better unless they are given exercises for their dizziness and balance problems. Staying active is very important and moving your head, even if it makes you dizzy, is one of the best ways to recover from your inner ear problem. Medicine is not usually needed; however, if your dizziness is very bad, your physical therapist may work with your doctor to try to make your symptoms better with medicine so you can continue doing the exercises. On the basis of your diagnosis, your best treatment may include eye and head exercises, walking exercises, balance exercises, and education about your dizziness problem. Exercises are progressed as you improve so that you are less dizzy when you sit still and also when you move around. Physical therapists are trained to customize your exercises for best recovery. For more information about the treatment of inner ear dizziness, contact a physical therapist who specializes in inner ear problems. INNER EAR TREATMENTS Evidence suggests that early treatment of inner ear problems is helpful in decreasing dizziness. Not all inner ear problems are the same and so not everyone will do every exercise. Your physical therapist will determine which exercises will help you the most on the basis of your dizziness and/or balance concerns. Figure 1 shows an example of a common exercise that might be prescribed for dizziness and imbalance as a result of your inner ear disorder.FIGURE 1: If you lose your balance in the dark or on uneven surfaces then you might do exercises that involve standing on a foam pad with your feet in different positions.This JNPT Perspectives for Patients is based on an article by Hall CD et al, titled “Vestibular Rehabilitation for Peripheral Vestibular Hypofunction: An Evidence-Based Clinical Practice Guideline” (J Neurol Phys Ther 2016) and was written by Courtney Hall, Susan Herdman, and Susan Whitney and illustrated by Maverick Dunavan.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.309
Teacher spread0.259 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2016
Admission routes1
Has abstractyes

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