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Record W2122145844 · doi:10.3122/jabfm.2015.01.120295

An iPhone-Assisted Particle Repositioning Maneuver for Benign Paroxysmal Positional Vertigo (BPPV): A Prospective Randomized Study

2015· article· en· W2122145844 on OpenAlexaffabout
B. Organ, Hao Liu, Matthew Bromwich

Bibliographic record

VenueThe Journal of the American Board of Family Medicine · 2015
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsBenign paroxysmal positional vertigoMedicinePhysical therapyPrimary careRandomized controlled trialVertigoAudiologyPhysical medicine and rehabilitationInternal medicineFamily medicineSurgery

Abstract

fetched live from OpenAlex

PURPOSE: The Epley particle repositioning maneuver (PRM) is an effective treatment for benign paroxysmal positional vertigo (BPPV), the most common cause of peripheral vertigo in primary care settings. The goal of this study was to determine whether the use of an iPhone application (DizzyFIX; Clearwater Clinical Ltd, Ottawa, Ontario, Canada) by medical students had a significant impact on the performance of the PRM. METHODS: We recruited senior medical students who had previously been trained in the management of BPPV and asked them to perform the PRM on a healthy volunteer. One half of the students used a real iPhone application, whereas the others used a sham application. The PRM performance scores of the 2 groups were compared. RESULTS: iPhone application users scored significantly higher on their PRM performance compared with controls (P < .0001) and performed the PRM significantly more slowly (P < .0001). CONCLUSIONS: Senior medical students performed a more correct PRM when assisted by the iPhone application. This application represents a significant improvement from standard medical school training using written instructions. Family physicians could also use this iPhone application for the quick and effective treatment of BPPV.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.667
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.058
GPT teacher head0.336
Teacher spread0.278 · 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

Citations25
Published2015
Admission routes2
Has abstractyes

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