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Record W4396513841 · doi:10.1177/2050313x241249910

Using virtual reality to manage pain and anxiety during dental treatments in patients with stroke: A case series

2024· article· en· W4396513841 on OpenAlexafffund
Essete Makonnen Tesfaye, Isabella P. Garito, Samantha Lewis-Fung, Dale Calabia, Robert Schlosser, Lora Appel

Bibliographic record

VenueSAGE Open Medical Case Reports · 2024
Typearticle
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsToronto East General HospitalToronto Rehabilitation InstituteYork UniversityUniversity Health Network
FundersToronto Rehabilitation InstituteYork University
KeywordsAnxietyMedicineDistractionVirtual realityDental ProcedureStroke (engine)UsabilityWorkflowPhysical therapyDentistryDental carePsychiatryPsychology

Abstract

fetched live from OpenAlex

Dental anxiety is common post-stroke, with many patients unable to receive standard anesthetics. Virtual reality has been increasingly used to manage pain and anxiety in dentistry, though its use in individuals with stroke is largely unexplored. A case series of two patients with a history of stroke and dental anxiety was conducted at a specialized dental clinic. Patients watched 360°-virtual reality videos in a dental chair using a head-mounted display. Outcomes (patient: dental anxiety and pain, reactions to virtual reality; dental team: system usability, impact on workflow) were assessed using a standard observation tool, questionnaires, and interviews. Both patients wore virtual reality throughout the procedure and reported that the device was comfortable, provided a distraction, and had potential to reduce anxiety/pain. The dentist reported a positive impact on patient anxiety and time to complete procedures, and intends to continue using virtual reality with other stroke patients and clinical populations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.015
GPT teacher head0.296
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations3
Published2024
Admission routes2
Has abstractyes

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Same venueSAGE Open Medical Case ReportsSame topicDental Anxiety and Anesthesia TechniquesFrench-language works237,207