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Record W4415257923 · doi:10.1055/s-0045-1810116

Telerehabilitation and Physical Therapy: Proposal for a Therapeutic Assessment Applied to Vestibular Dysfunctions

2025· article· en· W4415257923 on OpenAlexaff
Lucas Barbosa de Araújo, Karla Vanessa Rodrigues Soares Menezes, Jully Israely de Azevedo Rodolfo, Maria Lira, Karyna Myrelly Oliveira Bezerra de Figueiredo Ribeiro

Bibliographic record

VenueInternational Archives of Otorhinolaryngology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversité de Montréal
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsTelerehabilitationVestibular rehabilitationRehabilitationAttendanceVestibular systemProtocol (science)

Abstract

fetched live from OpenAlex

Introduction: Telerehabilitation has been used in several areas of physical therapy, including for respiratory, neurological, and musculoskeletal functions of patients with coronavirus disease 2019 (COVID-19), after stroke, and after hospital discharge (respectively). However, a few studies investigated protocols for assessing vestibular dysfunctions using teleconsultation. Objective: To propose a protocol for remote physical therapy assessment of vestibular dysfunctions. Methods: . Four physical therapists with experience in the vestibular rehabilitation field discussed the collected data and suggested adaptations for remote clinical and functional tests to assess patients with vestibular dysfunctions. Results: The proposed protocol for remote assessment of vestibular dysfunctions comprised anamnesis, adaptations of nine oculomotors, two static balance, and one dynamic gait balance tests, a questionnaire assessing the impact of dizziness on quality of life, and observation of cervical mobility. Conclusion: The protocol may be a valuable tool to assess and monitor the care of patients with vestibular dysfunction, reducing healthcare costs for the therapist and patient and enabling the attendance of those with difficulties in traveling to the rehabilitation center or needing isolation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.415

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 head0.014
GPT teacher head0.311
Teacher spread0.297 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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