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Record W2803278039 · doi:10.31415/bjns.v1i1.16

Avaliação de produto fitoterápico de uso tópico na dor musculoesquelética em ginastas

2018· article· pt· W2803278039 on OpenAlexaboutno aff
André Luis Aguiar Silva, Bruna Laurino Cangueiro, Carolina Bernardo araujo da Silva, Giovanna Santana Pinto Santos, Guilherme Giane Peniche, Carlos Rocha Oliveira, Valéria Maria de Souza Antunes

Bibliographic record

VenueBrazilian Journal of Natural Sciences · 2018
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicineArt

Abstract

fetched live from OpenAlex

Esta pesquisa avaliou o efeito e atividade terapêutica do produto fitoterápico de uso tópico da comaçafrão-da-terra (Curcuma longa L.), erva baleeira (Cordia verbeneacea), pimenta (Capsicum frutescensL) e gengibre (Zingiber officinale) na dor musculoesquelética de ginastas da academia YASHI e do BrasilFutebol Clube durante sete semanas por meio da escala visual de dor, questionário complementar e doMcGill o nível e intensidade da dor. As ginastas foram divididas em Grupo A (produto fitoterápico) eGrupo B (pomada placebo). As ginastas do grupo A apresentaram uma melhora de 68% nas regiões referidas de dor, enquanto as ginastas do grupo B apresentaram uma melhora de 25% da dor avaliada através da Escala Analógica Visual. A média de variação do índice de dor através do McGill referente ao Grupo A foi de 10%, enquanto a do Grupo B foi de 16%. Acredita-se que a redução da dor no grupo placebo pode ter ocorrido devido à diminuição do estresse gerando efeitos fisiológicos que contribuíram para uma possível liberação de opióides no alívio da dor, também pode ser levado em consideração o processo de auto cura devido à presença terapêutica. Concluiu-se que o produto fitoterápico de uso tópico tematividade terapêutica e é eficaz para o tratamento de dor musculoesquelética.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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.

Study designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

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