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Record W4416707620 · doi:10.5327/cbn241466

Seasonality of fatigue in Parkinson’s disease in a high temperature location

2024· article· W4416707620 on OpenAlexaboutno aff
Luma Rodrigues da Silva, Lara Beatriz Alves Batista, Pedro Henrique Ximenes Ramalho Barros, ISABEL MARIA OLIVEIRA MACEDO LIMA, Sabrina Ruthiele Santos de Carvalho, Jonatas Paulino da Cunha Monteiro Ribeiro, Samuel de Castro Campos, José Henrique de Melo Feitosa, Kelson James Almeida

Bibliographic record

VenueArquivos de Neuro-Psiquiatria · 2024
Typearticle
Language
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)DiseaseRating scaleWilcoxon signed-rank testObservational studyScale (ratio)SeasonalityNormality

Abstract

fetched live from OpenAlex

Background: Fatigue is one of the most common and disabling non-motor symptoms in Parkinson’s disease (PD), and its assessment is important to measure the quality of life of these patients. Simultaneously, rising temperatures due to global warming may aggravate conditions such as fatigue. The city of Teresina has a warming scenario that will only become a reality in some regions of the globe in the next decade, making it relevant to analyze the influence of temperature on neurological diseases in this city. Objective: To assess the severity of fatigue and quality of life in patients with Idiopathic Parkinson’s Disease (IPD) according to seasonal variations in a high temperature location. Methods: This is a longitudinal observational study. It included patients with IPD according to the UK Parkinson’s Disease Society Brain Bank and Movement Disorders Society criteria, followed at a movement disorder center in Teresina-PI. Assessments took place in the high temperature period (September to December) and the mild temperature period (January to April), using the Unified Parkinson’s Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Parkinson’s Disease Questionnaire-39 (PDQ-39), and Fatigue Severity Scale (FSS). The R software (R Core Team) was used for statistical data analysis. The normality assumption of the data was verified by the Shapiro-Wilk test. The Wilcoxon test was used to compare the UPDRS scale values in the high and mild temperature periods. The paired t-test was used to compare the levodopa equivalent dose (LED) values and the FSS and PDQ-39 scales in the high temperature period (HTP) and mild temperature period (MTP). The criterion for a statistically significant difference was p-value <0.05. Results: A sample of 16 patients with a mean age of 65 years, 75% male, with a mean duration of symptoms of 9.06 years and a mean diagnosis time of 8.12 years, the mean MoCA score was 18.81. The mean UPDRS was 33.18 in HTP and 31.64 in MTP (p=0.9645). The average LED in HTP was 617.31, and in MTP was 667.37 (p=0.1730). The average FSS in HTP was 4.56, while in MTP was 4.53 (p=0.9227). The average PDQ-39 was 46.33 in HTP and 45.36 in MTP (p=0.6906). There were no statistically significant differences between the high and mild temperature periods. Conclusion: The severity of fatigue and quality of life in patients with Idiopathic PD did not differ between seasons in a high temperature location. The restricted sample became the current findings limited.

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.285
Teacher spread0.265 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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