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Record W3217274425 · doi:10.3390/ijerph182212240

Development and Validation of a Graded Motor Imagery Intervention for Phantom Limb Pain in Patients with Amputations (GraMI Protocol): A Delphi Study

2021· article· en· W3217274425 on OpenAlexfundno aff
Sandra Rierola‐Fochs, Luz Adriana Varela‐Vásquez, José Antonio Merchán-Baeza, Eduard Minobes‐Molina

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
FundersUniversity of Victoria
KeywordsPhysical therapyProtocol (science)Intervention (counseling)MedicinePhysical medicine and rehabilitationDelphi methodPain scaleNeurorehabilitationInterquartile rangeRehabilitationComputer scienceSurgeryNursingArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Phantom limb pain can be defined as discomfort or pain in a missing part of the limb. The aims of this study were to develop and validate, through a Delphi methodology, a graded motor imagery protocol in order to reduce phantom limb pain. METHOD: Physiotherapists and/or occupational therapists with experience in research and a minimum clinical experience of five years in the field of neurorehabilitation and/or pain were recruited by part of a group of experts to assess the intervention. The study was conducted through an online questionnaire, where experts assessed each aspect of the intervention through a Likert scale. As many rounds as necessary were carried out until consensus was reached among experts. RESULTS: A total of two rounds were required to fully validate the intervention. During the second round, the relative interquartile range of all aspects to be assessed was less than 15%, thus showing a consensus among experts and with good concordance (Kappa index of 0.76). CONCLUSION: Experts validated a graded motor imagery intervention of phantom limb pain in patients with amputations (GraMi protocol). This intervention can help to homogenize the use of graded motor imagery in future studies and 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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.078
GPT teacher head0.411
Teacher spread0.333 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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