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Record W4392651170 · doi:10.53730/ijhs.v8ns1.14796

influence of virtual reality approach on phantom pain in trans-tibial amputation

2024· article· en· W4392651170 on OpenAlexaboutno aff
Mohamed Elgendy, Mohamed H. Helal, Ahmed Habib

Bibliographic record

VenueInternational Journal of Health Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPhantom painAmputationImaging phantomVirtual realityPhantom limb painPhantom limbMedicineComputer sciencePsychologyPhysical medicine and rehabilitationHuman–computer interactionSurgeryRadiology

Abstract

fetched live from OpenAlex

Background: Phantom limb pain (PLP): is a common complaint after lower limb amputation can be defined as discomfort or pain in a missing part of the limb. This study was conducted in the research laboratory section, Faculty of Physical Therapy, Cairo University, Giza, Egypt. Purpose: To investigate the effect of Virtual reality (VR) on phantom limb pain and lower limb Function in trans tibial amputation. Methods: sixty patient with phantom pain were enrolled into 2 equal groups: a study group and a control group. Outcome measures included pain intensity level was measured by McGill pain questionnaire and Lower Extremities Function was measured by Lower Extremities Function Scale. That were assessed at baseline and 4 weeks postintervention. Results: A statistically significant effect (p < 0.0001) of treatment and time was revealed in both groups for all measured variables. Between-group analysis implied a higher improvement in post-intervention results in group B (p < 0.05). Conclusion: This study indicated that Using VR with conventional treatment is more effective in case of Phantom pain in trans-tibial amputation that in improving pain intensity level (reduce pain) and Lower Extremities Function than conventional treatment alone.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.051
GPT teacher head0.400
Teacher spread0.349 · 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 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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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