MétaCan
Menu
Back to cohort
Record W7061898773

Relação entre o risco de sarcopenia e de disfagia orofaríngea em pacientes com Doença de Parkinson

2023· other· pt· W7061898773 on OpenAlexaboutno aff

Bibliographic record

VenueLume (Universidade Federal do Rio Grande do Sul) · 2023
Typeother
Languagept
FieldEngineering
TopicAdvanced Power Generation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSarcopeniaDysphagiaSwallowingRisk factorIntestinal malabsorption
DOInot available

Abstract

fetched live from OpenAlex

Objetivo: Relacionar o risco de sarcopenia com o risco de disfagia orofaríngea em pacientes com Doença de Parkinson. Métodos: Estudo transversal. Coleta realizada entre setembro/2022 à fevereiro/2023. Utilizado Montreal Cognitive Assessment para rastreio cognitivo. Instrumentos utilizados na avaliação do risco de sarcopenia e de disfagia: SARC-F e Força de Preensão Palmar, e para o risco de disfagia o Eating Assessment Tool e Swallowing Disturbance Questionnaire. Incluídos indivíduos com diagnóstico de Doença de Parkinson, com idade maior ou igual a 18 anos, oriundos do Ambulatório de Fonoaudiologia Adulto Degenerativa ou de Distúrbios do Movimento do Serviço de Neurologia do Hospital de Clínicas de Porto Alegre. Excluídos os pacientes com doenças neurológicas associadas, alterações esofágicas e diagnóstico de demência. Resultados: A amostra final foi composta por 35 pacientes, classificados em três grupos: 13 (37,14%) sem sarcopenia, 10 (28,57%) com sugestivo de sarcopenia e 12 (34,28%) com risco de sarcopenia. Não houve relação entre a sarcopenia e a autoavaliação do risco de disfagia em indivíduos com Doença de Parkinson. Entretanto, é possível observar que o grupo de pacientes com risco de sarcopenia apresenta maior risco para disfagia. Conclusão: O risco de sarcopenia e de disfagia não relacionaram-se. O risco de sarcopenia relacionou-se com os dados clínicos da Doença de Parkinson.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueLume (Universidade Federal do Rio Grande do Sul)Same topicAdvanced Power Generation TechnologiesFrench-language works237,207