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Record W6990713290

Efectivitat d'un protocol d'intervenció domiciliària (protocol GraMI) basat en imatge motora graduada en el tractament del dolor del membre fantasma en persones que han patit una amputació

2024· article· ca· W6990713290 on OpenAlexfundno aff

Bibliographic record

VenueRIUVic · 2024
Typearticle
Languageca
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
FundersUniversity of Victoria
KeywordsProtocol (science)Internet of ThingsAcute pain
DOInot available

Abstract

fetched live from OpenAlex

Actualment, es registra una elevada prevalença d'amputacions, principalment atribuïda al procés d'envelliment de la població i als accidents de trànsit, amb la previsió d'un augment en els pròxims anys. Després d'una amputació, el 64% de les persones experimenten dolor del membre fantasma, una sensació dolorosa o desagradable en la part del cos que ha estat amputada. Aquesta tesi doctoral presenta un protocol d'intervenció domiciliària, conegut com a protocol GraMI, dissenyat, desenvolupat i validat en base a l’evidència científica i clínica més recent. Aquest protocol permet que les persones realitzin la intervenció des del seu domicili mitjançant l'ús de tecnologies digitals, eliminant la necessitat de desplaçament. Els diferents estudis realitzats mostren que el protocol GraMI és efectiu en la reducció del dolor del membre fantasma en persones que han patit una amputació. A més, aquesta efectivitat es manté fins a dotze setmanes després de la intervenció.

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)0.166
metaresearch head score (Gemma)0.217
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.166
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1660.217
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.002
Science and technology studies0.0050.004
Scholarly communication0.0070.005
Open science0.0030.007
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0410.012

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 head0.008
GPT teacher head0.294
Teacher spread0.286 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2024
Admission routes1
Has abstractyes

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