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Record W4416953724 · doi:10.3389/fmed.2025.1658190

Monitoring pain during use of virtual reality in debridement procedures of vascular wounds in outpatient care settings

2025· article· en· W4416953724 on OpenAlexaboutno aff
Joanna Przybek-Mita, Dariusz Bazaliński, Anna Surmacz, Anna Kołodziej, Julia Bryła

Bibliographic record

VenueFrontiers in Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDebridement (dental)Virtual realityWound careClinical PracticeMinimally invasive proceduresQualitative analysisPostoperative pain

Abstract

fetched live from OpenAlex

Introduction Virtual Reality (VR) is an advanced technological system which, besides its use in entertainment and education, has been permanently introduced over the past quarter-century into physical and psychiatric rehabilitation as well as medicine. Its effectiveness has been demonstrated in pain management during medical and rehabilitation procedures in burn patients, alleviation of cancer pain, and reduction of labor pain. VR is increasingly used during routine medical procedures in children, such as blood sampling, intravenous cannulation, and vaccination, and holds promise for chronic pain management. Objective The aim of this study was to assess pain during the treatment of hard-to-heal wounds of vascular origin using VR as an adjunct non-pharmacological pain therapy. Materials and methods An observational study was conducted in a chronic wound care clinic involving 100 patients. The mean age was 68.02 ± 10.0 years. All participants had hard-to-heal wounds of vascular origin. The mean wound duration was 7.16 ± 5.08 months, with an average wound area of 39.18 ± 71.83 cm 2 (range 2 cm 2 to 625 cm 2 ). Patients were randomly assigned to a group distracted with VR goggles and a control group receiving standard care without VR. Pain intensity was assessed using the Numeric Rating Scale (NRS) at three time points, and the McGill Pain Questionnaire (MPQ) before the procedure, during wound debridement, and 10 min after completion. Results Statistically significant differences were observed in pain assessment before and during wound debridement ( p < 0.05). In the VR group, higher pain scores were recorded before wound care compared to the control group. Ten minutes before wound debridement, the mean pain intensity in the VR group was 2.60 ± 1.63, higher than 2.0 ± 1.53 in the control group. During wound debridement, pain intensity was higher in the control group (4.94 ± 1.53) compared to the VR group (4.32 ± 2.17). Pain intensity 10 min after debridement was similar in both groups: control (2.24 ± 1.41) and VR (2.36 ± 1.71). These findings support the hypothesis that VR goggles reduce pain intensity. No statistically significant differences in NRS pain scores were found between patients with different wound types in either group ( p > 0.05). Variables such as wound duration and wound size influenced pain levels 10 min before wound care. No association was found between sex and pain intensity ( p > 0.005). Conclusion Increased pain during procedures involving manipulation of damaged tissues and wound debridement is a common phenomenon. This study confirmed that the use of VR goggles reduces perceived pain levels. The assessment of pain experience and intensity varies depending on the assessment tools used; therefore, a combined quantitative and qualitative evaluation is recommended to accurately determine the usefulness of innovative tools in clinical practice.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0020.000

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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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 designObservational
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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