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Record W4399304292 · doi:10.56083/rcv4n5-236

USO DE MEDICAMENTOS NO TRATAMENTO DA DOENÇA DE PARKINSON E IMPLICAÇÕES DE TRATAMENTOS ALTERNATIVOS

2024· article· pt· W4399304292 on OpenAlexaff
João Gomes Pontes Neto

Bibliographic record

VenueRevista Contemporânea · 2024
Typearticle
Languagept
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicineParkinson's diseaseInternal medicineDisease

Abstract

fetched live from OpenAlex

O presente estudo tem como objetivo primordial disseminar conhecimentos sobre a doença de Parkinson e as implicações dos poucos medicamentos atualmente disponíveis para seu tratamento. Há indícios de escassez de investimentos em novas pesquisas de tratamentos farmacológicos e a falta de atenção voltada para as necessidades dos pacientes afetados por essa doença neurodegenerativa. Ao abordar o Parkinson, é fundamental compreender todas as formas de terapia disponíveis, considerando que a estabilização da condição ao longo do tempo requer não apenas tratamento farmacológico, mas também outras modalidades terapêuticas. O texto explorará algumas modalidades terapêuticas para o tratamento da Doença de Parkinson, examinando tanto seus aspectos favoráveis quanto desfavoráveis, bem como potenciais desafios relacionados à adesão aos medicamentos. O tratamento dessa enfermidade demanda cuidado minucioso e requer uma abordagem empática e atenciosa. A pesquisa de artigos seleciona publicações entre 2019 e 2023 na plataforma PubMed, utilizando descritores como: Parkinson disease, treatment of tremors, cannabinoids, levodopa e deep brain stimulation. Após aplicação de critérios de inclusão e exclusão, foram selecionados 9 estudos relevantes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.326
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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