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Interferential Current Electrotherapy is More Effective Thanthan TENS VIF in Cancer Pain Management

2023· article· en· W4386710407 on OpenAlexaboutno aff
Juliana Carvalho Schleder, Fernanda Aparecida Verner, Luiz Cláudio Fernandes, Débora Melo Mazzo

Bibliographic record

VenueJournal of Health Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsElectrotherapyTranscutaneous electrical nerve stimulationMedicineCancer painAnalgesicMcGill Pain QuestionnairePhysical therapyCancerAnesthesiaInternal medicineVisual analogue scale

Abstract

fetched live from OpenAlex

Abstract Cancer diagnosis is increasing rapidly worldwide and pain is a common feature reported by cancer patients. Therapeutical approach on cancer pain is complex where less invasive methods with little side effects have been sought. The aim of this study was to compare transcutaneous electrical nerve stimulation (TENS) and interferential current (IC) therapies effects on cancer pain. Double blind study with 81 cancer pain patients. Subjects were set up into two groups: one treated with TENS VIF (n=42) and other with IC (n=39). Age, gender, duration of pain, tumor site and histology, medications, treatments, Karnofsky score and clinical state were evaluated. Pain was measured by EMADOR and McGill scores. Electroanalgesia was performed for 30 minutes, the equipments used were Neurodyn III Ibramed® and Neurovector generation 2000 Ibramed®. Electrodes were placed where there was higher intensity of pain according to what was shown by the patient through EMADOR, and each one got only one electrotherapy session. Pain intensity was significantly reduced in both groups (p<0.001) soon after and until 6th hour post electrotherapy. IC group had better results at 4th, 5th (p<0.001) and 6th hour (p=0.022). McGill score in TENS VIF group was significant until 4th hour and in the IC group was highly significant in all evaluated times (p<0.001). Analgesic effect of TENS VIF and IC electrotherapy was clinically effective, however, IC did cause better results regarding analgesia duration. Keywords: Cancer Pain. Analgesia. Physical Therapy Modalities. Transcutaneous Electric Nerve Stimulation. ResumoO diagnóstico de câncer está aumentando rapidamente em todo o mundo e a dor é uma característica comum relatada por pacientes com câncer. A abordagem terapêutica da dor oncológica é complexa onde métodos menos invasivos e com poucos efeitos colaterais têm sido buscados. O objetivo deste estudo foi comparar os efeitos das terapias de estimulação elétrica nervosa transcutânea (TENS) e corrente interferencial (IC) na dor oncológica. Estudo duplo-cego com 81 pacientes com dor oncológica. Os indivíduos foram divididos em dois grupos: um tratado com TENS VIF (n=42) e outro com IC (n=39). Idade, sexo, duração da dor, local do tumor e histologia, medicamentos, tratamentos, pontuação de Karnofsky e estado clínico foram avaliados. A dor foi mensurada pelos escores EMADOR e McGill. A eletroanalgesia foi realizada por 30 minutos, os equipamentos utilizados foram Neurodyn III Ibramed® e Neurovector geração 2000 Ibramed®. Os eletrodos foram colocados onde havia maior intensidade de dor de acordo com o apresentado pelo paciente através da EMADOR. A intensidade da dor foi significativamente reduzida em ambos os grupos (p<0,001) logo após e até a 6ª hora pós-eletroterapia. O grupo CI teve melhores resultados na 4ª, 5ª (p<0,001) e 6ª hora (p=0,022). O escore de McGill no grupo TENS VIF foi significativo até a 4ª hora e no grupo IC foi altamente significativo em todos os tempos avaliados (p<0,001). O efeito analgésico da TENS VIF e da eletroterapia com IC foi clinicamente eficaz, porém a IC trouxe melhores resultados quanto à duração da analgesia. Palavras-chave: Dor do Câncer. Analgesia. Modalidades de Fisioterapia. Estimulação Elétrica Nervosa Transcutânea.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.045
GPT teacher head0.438
Teacher spread0.393 · 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 designNon-randomized trial
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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Citations2
Published2023
Admission routes1
Has abstractyes

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