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Record W2122002109 · doi:10.1586/erd.10.30

Self-treatment of benign paroxysmal positional vertigo with Dizzyfix™, a new dynamic visual device

2010· article· en· W2122002109 on OpenAlexaboutno aff
D. Brehmer

Bibliographic record

VenueExpert Review of Medical Devices · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsnot available
Fundersnot available
KeywordsBenign paroxysmal positional vertigoMedicineVertigoPhysical medicine and rehabilitationAudiologyComputer scienceSurgery

Abstract

fetched live from OpenAlex

Benign paroxysmal positional vertigo is one of the most common disorders of the vestibular system. It is characterized by episodes of recurrent vertigo triggered by head movements or position changes associated with nystagmus. There is scientific evidence that in the majority of cases this condition responds well to the particle repositioning maneuver (PRM) correctly performed by the physician. However, the PRM needs to be repeated in approximately 30% of the cases. Although the maneuver is simple, patients often find it difficult to perform correctly as self-treatment, with the result that it fails to bring about an improvement in the symptoms. DizzyFix (Clearwater Clinical Limited, Canada) is the name given to a new dynamic visual device designed to provide a visual representation of the PRM based on the canalith theory. The DizzyFiX consists of a specially curved acrylic tube containing a nontoxic viscous fluid and a bead, the purpose of which is to help the patient and the inexperienced physician to perform the PRM correctly. A randomized clinical trial has shown that it reliably enables the maneuver to be performed correctly, and a study investigating the effectiveness of patient self-treatment of benign paroxysmal positional vertigo with the device in comparison with standard office treatment revealed both techniques to be equally effective. The device has now been approved by the US FDA.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.011
GPT teacher head0.332
Teacher spread0.321 · 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 designNot applicable
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

Citations4
Published2010
Admission routes1
Has abstractyes

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