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Record W3030152549 · doi:10.1002/mds.28176

<scp>COVID</scp>‐19 in Parkinson’s Disease Patients Living in Lombardy, Italy

2020· article· en· W3030152549 on OpenAlexaff
Alfonso Fasano, Emanuele Cereda, Michela Barichella, Erica Cassani, Valentina Ferri, Anna Zecchinelli, Gianni Pezzoli

Bibliographic record

VenueMovement Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsOntario Brain InstituteToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)CohortDiseasePediatricsPopulationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Cohort studyInternal medicineInfectious disease (medical specialty)Environmental health

Abstract

fetched live from OpenAlex

BACKGROUND: It is unknown whether patients with PD are at greater risk of COVID-19, what their risk factors are, and whether their clinical manifestations differ from the general population. OBJECTIVES: The study aimed to address all these issues. METHODS: In a case-controlled survey, we interviewed 1,486 PD patients attending a single tertiary center in Lombardy, Italy and 1,207 family members (controls). RESULTS: One hundred five (7.1%) and 92 controls (7.6%) were identified as COVID-19 cases. COVID-19 patients were younger, more likely to suffer from chronic obstructive pulmonary disease, to be obese, and vitamin D nonsupplemented than unaffected patients. Six patients (5.7%) and 7 family members (7.6%) died from COVID-19. Patients were less likely to report shortness of breath and require hospitalization. CONCLUSIONS: In an unselected large cohort of nonadvanced PD patients, COVID-19 risk and mortality did not differ from the general population, but symptoms appeared to be milder. The possible protective role of vitamin D supplementation warrants future studies. © 2020 International Parkinson and Movement Disorder Society.

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 score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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

Citations147
Published2020
Admission routes1
Has abstractyes

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