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Record W3080984610 · doi:10.1002/mds.28293

The Impact of <scp>COVID</scp>‐19 on Access to Parkinson's Disease Medication

2020· article· en· W3080984610 on OpenAlexaff
Julia L.Y. Cheong, Zhao Hang Keith Goh, Connie Marras, Caroline M. Tanner, Meike Kasten, Alastair J. Noyce

Bibliographic record

VenueMovement Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersBarts Charity
KeywordsCoronavirus disease 2019 (COVID-19)Parkinson's disease2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineDiseaseBetacoronavirusVirologyInternal medicineOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Many countries have implemented drastic measures to fight the COVID-19 pandemic. Restrictions and diversion of resources may have negatively affected patients with Parkinson's disease (PD). Our aim was to examine whether COVID-19 had an impact on access to PD medication by region and income. METHODS: This study was conducted as part of a survey sent to members of the Movement Disorders Society focusing on access to PD medication globally. RESULTS: Of 346 responses, 157 (45.4%) agreed that COVID-19 had affected access to PD medication, while 189 (54.6%) disagreed. 22.8% of high-income and 88.9% of low-income countries' respondents agreed that access to PD medication was affected by COVID-19. 59% of all 'yes' respondents reported increased disability of patients as an impact. CONCLUSIONS: Access to PD medication is likely to have been affected by COVID-19 and result in deterioration of patients' symptomatic control. Resource-poor countries appear to be disproportionately affected compared to more affluent countries. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of 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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.129
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.319
Teacher spread0.292 · 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 teacher head, 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

Citations48
Published2020
Admission routes1
Has abstractyes

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