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Diagnóstico e manejo da demência da doença de Parkinson e demência com corpos de Lewy: recomendações do Departamento Científico de Neurologia Cognitiva e do Envelhecimento da Academia Brasileira de Neurologia

2022· article· pt· W4312067485 on OpenAlexaboutno aff
Jacy Bezerra Parmera, Vítor Tumas, Henrique Ballalai Ferraz, Mariana Spitz, Maira Tonidandel Barbosa, Jerusa Smid, Breno José Alencar Pires Barbosa, Lucas Porcello Schilling, Márcio Luiz Figueredo Balthazar, Leonardo Cruz de Souza, Francisco Assis Carvalho Vale, Paulo Caramelli, Paulo Henrique Ferreira Bertolucci, Márcia Lorena Fagundes Chaves, Sônia Maria Dozzi Brucki, Ricardo Nitríni, Raphael Machado Castilhos, Norberto Anízio Ferreira Frota

Bibliographic record

VenueDementia & Neuropsychologia · 2022
Typearticle
Languagept
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsParkinson's diseaseMedicinePsychologyDiseasePathology

Abstract

fetched live from OpenAlex

Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB) represent the second most common type of degenerative dementia in patients aged 65 years and older, leading to progressive cognitive dysfunction and impaired quality of life. This study aims to provide a consensus based on a systematic Brazilian literature review and a comprehensive international review concerning PDD and DLB. Moreover, we sought to report on and give recommendations about the best diagnostic approaches focusing on primary and secondary care. Based on the available data, we recommend clinicians to apply at least one brief global cognitive instrument to assess PDD, such as the Mini-Mental State Examination and preferably the Montreal Cognitive Assessment and the Addenbrooke's Cognitive Examination-Revised. Validated instruments to accurately assess functional abilities in Brazilian PD patients are still incipient. Further studies should focus on biomarkers with Brazilian cohorts.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0030.002
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.034
GPT teacher head0.306
Teacher spread0.272 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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