Evidence map›Paper›PMID 32382948›Full record

ReviewDrugs2020

Therapeutic Strategies to Treat or Prevent Off Episodes in Adults with Parkinson's Disease.

Nirosen Vijiaratnam, Thomas Foltynie

Abstract readReview
PubMed Publisher
In one paragraph

Review in Drugs, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  10. Inhaled levodopa for threatening impending OFF episodes in managing Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nirosen VijiaratnamDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.
Thomas FoltynieDepartment of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK. t.foltynie@ucl.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease is a chronic, neurodegenerative disease, which manifests with a mixture of motor, cognitive and behavioural symptoms. Levodopa is the most effective antiparkinsonian treatment to date, although chronic use engenders a mixture of complications in a substantial proportion of patients. Amongst these is the occurrence of episodes of worsening symptoms-'off' phenomena. These episodes can manifest with either motor or non-motor symptoms or a combination of these features and have been found to have profound impacts on patients' quality of life. Although preventative measures are poorly evidenced, avoiding excessive total daily levodopa intake in selected populations that are deemed to be of a higher risk for developing these episodes warrants further exploration. Methods to improve levodopa bioavailability and delivery to the brain are currently available and are of value in addressing these episodes once they have become established. These include modifications to levodopa formulations as well as the use of complimentary agents that improve levodopa bioavailability. The deployment of device-assisted approaches is a further dimension that can be considered in addressing these debilitating episodes. This review summarises the clinical manifestations of 'off' phenomena and the current approaches to treat them. Although we briefly discuss clinical advances on the horizon, the predominant focus is on existing, established treatments.

Indexed as

AdultAntiparkinson AgentsHumansParkinson DiseaseAntiparkinson Agents

Identifiers

PMID32382948

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.