Telerehabilitation and Physical Therapy: Proposal for a Therapeutic Assessment Applied to Vestibular Dysfunctions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".